Social Network Models for Enhancing Reference-Based Search Engine Rankings

Abstract

This chapter elaborates on a twofold approach on models of web search engine retrieval and more specifically the way information resources are ranked in the query results. In particular, current models of information retrieval are blind to the social context that surrounds information resources thus do not consider the trustworthiness of their authors when they present the query results to the users. Following this point we elaborate on the basic intuitions that highlight the contribution of the social context - as can be mined from social network positions for instance - into the improvement of the rankings provided in reference based search engines. A review on ranking models in web search engine retrieval along with social network metrics of importance such as prestige and centrality is provided as a background. Then a presentation of recent research models that utilize both contexts is provided along with a case study in the internet based encyclopedia Wikipedia based on the social network metrics.

Full text

Social Network Models for Enhancing Reference Based
Search Engine Rankings
Nikolaos Korfiatis1, Miguel-Angel Sicilia2, Claudia Hess3, Klaus Stein3,
Christoph Schlieder3
1 Department of Informatics
Copenhagen Business School (CBS)
Howitzvej 60, DK-2000 Frederiksberg (Copenhagen)
Denmark
2 Department of Computer Science
University of Alcala
Ctra. Barcelona, km. 33.6 – 28871
Alcala de Henares (Madrid)
Spain
3 Laboratory for Semantic Information Technology
University of Bamberg
Feldkirchenstraße 21, D-96045 Bamberg
Germany
Abstract
This chapter elaborates on a twofold approach on models of web search engine retrieval
and more specifically the way information resources are ranked in the query results. In
particular, current models of information retrieval are blind to the social context that
surrounds information resources thus do not consider the trustworthiness of their
authors when they present the query results to the users. Following this point we
elaborate on the basic intuitions that highlight the contribution of the social context – as
can be mined from social network positions for instance – into the improvement of the
rankings provided in reference based search engines. A review on ranking models in
web search engine retrieval along with social network metrics of importance such as
prestige and centrality is provided as a background. Then a presentation of recent
research models that utilize both contexts is provided along with a case study in the
internet based encyclopedia Wikipedia based on the social network metrics.
Key-Words
Search Engines, Rankings, HITS , PageRank, Social Networks, Centrality

Social Network Models for Enhancing Reference Based Search Engine Rankings
Chapter Contents
1 Introduction .............................................................................................................. 3
2 Background and Motivation ..................................................................................... 5
2.1 Formal Representations .................................................................................... 7
2.2 Network Modes and Data................................................................................. 8
3 Reference Based Rankings in Web Context............................................................. 9
3.1 PageRank........................................................................................................ 10
3.2 HITS ............................................................................................................... 11
3.3 Other Approaches........................................................................................... 12
4 Linking and evaluating users.................................................................................. 13
4.1 Sociometric Status and Prominence ............................................................... 13
4.1.1 Prestige ................................................................................................... 14
4.1.2 Centrality ................................................................................................ 15
4.2 Cliques and Cohesive Subgroups ................................................................... 17
4.3 The FOAF vocabulary.................................................................................... 18
5 Evaluating web page importance through the analysis of social ties. .................... 20
5.1 Integrating imprecise expressions of the social context in PageRank............ 20
5.2 Integrating trust networks and document reference networks........................ 23
6 Case study: Evaluating Contributions in the Wikipedia......................................... 28
6.1 Contributor Degree Centrality........................................................................ 30
6.2 Article Degree Centralization......................................................................... 31
6.3 Interpretation of the results............................................................................. 32
7 Conclusions & Outlook .......................................................................................... 34
8 Biographical Notes ................................................................................................. 34
9 References .............................................................................................................. 36
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Social Network Models for Enhancing Reference Based Search Engine Rankings
1 Introduction
Since the introduction of information technology, information retrieval (IR) has been an
important branch of computer and information science mainly due to the ability to
reduce the time required by a user to gather contextualized information and knowledge
(Baeza-Yates & Ribeiro-Neto, 1999). With the introduction of hypertext (Conklin,
1987), information retrieval methods and technologies have been able to increase their
accuracy because of the high amount of meta-information available for the IR system to
exploit. That is not only information about the documents per se but information about
their context and popularity. However the development of the World Wide Web has
introduced another dimension to the IR domain by exposing the social aspect of
information (Brown & Duguid, 2002) produced and consumed by humans in this
information space.
Current ranking methods in information retrieval – which are used in web search
engines as well - exploit the references between information resources such as the
hypertextual (hyperlinked) context of web pages in order to determine the rank of a
search result (Dhyani, Keong, & Bhowmick, 2002; Faloutsos, 1985). The well-known
PageRank algorithm (Page, Brin, Motwani, & Winograd, 1998 ; Brin & Page, 1998) has
proved to be a very effective paradigm for ranking the results of Web search algorithms.
In the original PageRank algorithm, a single PageRank vector is computed, using the
link structure of the Web, to capture the relative “importance” of Web pages,
independent of any particular search query. Nonetheless, the assumptions of the original
PageRank are biased towards measuring external characteristics. In fact, Page, Brin,
Motwani, & Winograd (Page, Brin, Motwani, & Winograd, 1998) conclude their article
with the sentence “The intuition behind PageRank is that it uses information which is
external to the Web pages themselves - their backlinks, which provide a kind of peer
review".
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Social Network Models for Enhancing Reference Based Search Engine Rankings
That is to say that backlinksa (i.e. incoming links) are considered as a positive
evaluation of the respective web site. The PageRank therefore does not distinguish
whether the user setting the link agrees with the content of the other webpage or
whether she or he disagrees. This fact underlines that current reference-based ranking
algorithms often do not take into consideration that an information resource is a result
of cognitive and social processes. In addition to its surrounding hyperlinks a social
context (Brown & Duguid, 2002) underlies the referencing of those resources.
This suggests that a critical point in improving link-based metrics would be that of
augmenting or weighting the pure backlink or reference model with social information,
provided that linking is in many cases influenced by social ties, and trustworthiness
critically depends on the social relevance or consideration of the authors of the pages.
Recently, several independent research has provided different models for this kind of
social network analysis as applied to ranking or assessing the quality of Web pages
(Sicilia & Garcia, 2005; Hess, Stein & Schlieder, 2006; Stein & Hess, 2005) or activity
spaces such as the usenet and wikis (Korfiatis & Naeve, 2005). Other approaches such
as those presented by Borner, Maru and Goldstone (Borner, Maru, & Goldstone, 2004)
have also dealt with integrating different networks by analyzing the simultaneous
growth of coauthor and citation networks in time.
The main objective of this chapter is to provide a survey and a generic framework for
such approaches as a roadmap for further research in this direction. Furthermore the
chapter attempts to provide a bridge to the areas of Social Network Analysis (SNA) and
Information Retrieval by highlighting the benefits of their integration in the case of a
web search engine.
To this end this chapter is organized as follows: Section 2 describes the basic intuition
behind the concepts and the definitions provided by this chapter. Section 3 provides an
insight to the classic models of citation and link based ranking methods such as
PageRank and HITS. Section 4 provides an insight to sociometric models that can be
used in order to evaluate users as well as a reference to the FOAF vocabulary which is
used to define social connections on the web. Section 5 provides an overview of two
a With the term backlink we refer to the backwards reference given to an object by another referring
object. The most well known type of a backlink is the citation provided to scientific articles and scholarly
work.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
exemplar hybrid ranking models which integrate in an equal way both the social and
hypertextual context. Then, Section 6 provides a case study of modeling
authoritativeness on data obtained from the English-language version of the Wikipedia,
by using some of the metrics discussed in Section 4. Finally section 7 presents the
conclusions and provides an outlook of future research directions which can be
exploited towards the combined consideration of hypertextual and social context in a
web IR system such as a web search engine.
2 Background and Motivation
The original intuition behind the design of the World Wide Web and the Hypertext
Markup (HTML) language is that authors can publish web documents which can
provide pointers to other documents available online. This simplified aspect of a
hypertext model adapted by Tim Berners-Lee in the original design of WWW (Berners-
Lee & Fischetti, 1999) has given to the web the morphology of an open publication
system which can evolve simultaneously by providing references and pointers to
existing documents available. However, as in the original publication contexts where
an author gains credibility due to the popularity of her or his productions/affiliations,
the credibility/appropriateness of a page on the web is to some extent correlated with its
popularity. What the first search engines on the web failed to capture was exactly this
kind of popularity which attributes the appropriateness to the query results of the search
engine.
Although advances in query processing have made the retrieval of the query results
computationally efficient (Wolf, Squillante, Yu, Sethuraman, & Ozsen, 2002) the
precision and the recall of the web document retrieval systems such as search engines is
under the negative effects of issues such as the ambiguity inherent to natural language
processing. Further, backlink models are blind to the authors of the pages, which entails
that every link is equally important for the final ranking. This overlooks the fact that
some links can be more relevant than others, depending on their authors or other
parameters such as affiliation with organizations, and even in some cases, the links may
have been created with a malicious purpose, such as spamming or the popular case of
“Google bombing” (Mathes, 2005).
Probably the best example where the above phenomena are manifested is the field of
scientometrics and in particular the scholar evaluation problem. Earlier from the
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Social Network Models for Enhancing Reference Based Search Engine Rankings
introduction of the impact factor by Garfield (Garfield, 1972) several researchers have
considered the development of metrics that can assist the evaluation of a scholar based
on the reputation of his scientific works usually denoted as references to his/her work
(Leydesdorff, 2001).
In particular the scholar evaluation problem can be formalized as follows:
Considering a scholar with a collection of scientific output represented by a set of
scholarly productions/publications as
S
{
}
n
aaaA ,, 21 K
=
then his/her research impact
B
is the aggregation if the impact of the individual scholarly works as:
)}(,),(),({ 21 n
aPaPaPB KΩ=
Where, Ω is an aggregation operation (e.g. averaging) over the popularity of
his/her research production . To the above formalization a set of open issues exist:
)( i
aP
i
a
 Regarding the variability of his/her research work how can we aggregate the
impact of his production to a representative and generally accepted number?
 How do we measure the popularity of the production? For example do we count
the same the reference provided to a research work by a technical report and
by a generally accepted “prestigious” journal?
Scientometrics provide an insight to open issues in the evaluation of the importance of
documents that represent the scientific production whereas in our case we look into the
evaluation of the importance of web pages. We follow the assumption that those web
pages/documents are productions of social entities which transpose a degree of
credibility from their social context.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
Figure 1: Layers of Context in the mixed mode network of authors or readers (blue) and web
resources (green).
Based on this assumption we develop in the section that follow, a basic framework that
underlines the core of the credibility models presented through the rest of this chapter
both for the social and hypertextual context. As can be seen in Figure 1 our framework
considers a kind of affiliation network (see definition bellow) where hypertext
documents (web pages) are connected to their authors.
2.1 Formal Representations
Before continuing with the models representing credibility in both social and
hypertextual contexts we should first have a look on the formalizations used in order to
be able to comprehend the metrics and the models using them.
A social/hypertextual network is formalized as a graph ),(: EVG
=
which is an ordered
pair of two sets. A set of vertices ),.....,,,( 321 n
VVVVV
=
which represents the social
entities/pages and a set of edges ),.....,,,( 211211 ij
EEEEE
=
where represents the
adjacent connection between the nodes i and
ij
E
j
. In abstract form the network structure
can be represented as a symmetric matrix (adjacency matrix) in which the nodes are
listed in both axes and a Boolean value is assigned to , which depicts the existence of
a relational tie between the entities, represented in .
ij
E
ij
E
Depending on the type of the network the adjacent connection may be:
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Social Network Models for Enhancing Reference Based Search Engine Rankings
 Edge: Indicates a connection between two nodes. In that case the graph is in the
general form: When the connection is directed the graph has the
general form denoting that the connection is a directional.
),(: EVG =
),,(: a
EEVG =
 Signed Edge: Indicates a connection between two nodes which is assigned with
a value. In that case the graph is in the general form ),,(: d
EEVG
=
where the
set is the value set for the mapping . When the connection is
directional the graph has the general form
d
Ed
EE →
),,,(: da EEEVG
=
where and
is the directed connection and value set respectively
a
E
d
E
An edge is often referenced in the literature as Arc. In a social network context, an edge
and an arc differ on the fact that edge denotes a reciprocal connection whereas arc is a
directed one. However when the network is a directed graph (digraph) those two terms
become synonymous.
Furthermore depending on the group size a relation can be dichotomous, trichotomous
or connecting subgroups.
 A dichotomous relation is a bivalence type of relational tie where the valued set
Ed is mapped to [0; 1]. Dichotomous relationships form units called dyads that
are used to study indirect properties between two individual entities. In the web
for example the referring link from one page to another represents a
dichotomous relation.
 A trichotomous or triad is a container of at most six dyads that can be either
signed or unsigned. Usually in a triad a dyadic relation is referenced by a third
entity which provides a point of common affiliation. For instance two pages are
referenced by a directory page which can be studied in a triad.
 A subgroup relation is a more complex relation that acts as a container of triads
where the members are interacting using a common flow or path.
2.2 Network Modes and Data
In principle network data consists of two types of variables: Structural and
Compositional. Compositional variables represent the attributes of the entities that form
apart the network. This kind of data can be for instance the theme of the webpage etc.
Structural variables define the ties between the network entities and the mode under
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Social Network Models for Enhancing Reference Based Search Engine Rankings
which the network is formed. Depending on the domain considered, different
terminologies can be used.
Structural variables Compositional variables
links Web pages/documents
References/citations Papers/articles
Table 1: Example structural and compositional variables for different applications/domains
Depending on the measurement of structural variables a network can have a mode. The
term mode refers to the number of entities which the structural variables address in the
network.
In particular we can have the two following types of networks:
 One-mode networks: This is the basic type of network where structural variables
address the relational ties between entities belonging on the same set. The web is
in an abstract form a one mode network linking web documents.
 Two-mode networks or affiliation networks: This type of networks contain two
set of entities e.g. authors and articles. An affiliation network is a special kind of
two mode network where at least one member of the set has a relational tie with
the member of the other set. E.g. an author has written an article. The networks
studied through the rest of this chapter are affiliation networks whereas authors
produce documents with which are affiliated.
In social network theory several other kind of network exist such as for instance ego-
centered networks or networks based on special dyadic relations. As aforementioned we
intent to study only affiliation networks such as the author – article network or the
reviewer-article network presented in section 5.
3 Reference Based Rankings in Web Context.
As noted in Sec.1, using the hyperlink structure of the web (or any linked document
repository) to compute document rankings is a very successful approach (visible in the
success of Google). The basic idea of link structure based ranking methods is that the
prominence of a document d
p can be determined by looking at the documents citing
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Social Network Models for Enhancing Reference Based Search Engine Rankings
d
p (and the documents cited by d
p)b. A paper cited by many other papers must be
somehow important otherwise it would not be cited so much. And a webpage linked by
many other pages gains prominence (visibility).
Another view is the random surfer model: if an user starts on an arbitrary web page,
follows some link to another page, follows a link from this page to a third one and so
on, the likelihood for getting a certain page will depend on two aspects: from how many
other pages it is linked and how visible the linking pages are.
3.1 PageRank
In 1976, Pinski and Narin (Pinski & Narin, 1976) computed the importance (rank) visd
of a scientific journal pd by using the weighted sum of the ranks vis
k
of the journals
p
k
with papers citing pd.c A slightly modified version of this algorithm (the
PageRank algorithm) is used by the search engine Googled to calculate the visibility
visd of a webpage pd:
visd1
pkRd
visk
Ck,
where Rd is the set of pages citing pd and C
k
is the set of pages cited by p
k
.
This resembles the idea of a random surfer: Starting at some webpage pa with
probability she follows one of Ca links while with 1 she stops following
links and jumps randomly to some other page. Therefore 1 can be considered as a
kind of basic visibility for every page. From the ranked document's perspective a page
pd can be reached by a direct (random) jump with probability 1 or by coming
from one of the pages p
k
Rd, where the probability to be on p
k
is vis
k
.
bTechnically a document reference network is a graph with documents as nodes and references (links) as
directed edges. The visibility of a node is determined by analyzing the link structure.
cThe basic idea of this algorithm goes back to Seelay (1949) who used it in sociometrics (first published
in L. Katz (1953), but without normalization by the number of outgoing edges and without damping term
(which was introduced by Charles H. Hubbell (1965).
dWe do not really know how google does its ranking. They claim that their ranking algorithm is based on
PageRank with modifications.
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The equation shown above gives a recursive definition of the visibility of a webpage
because visa depends on the visibility of all pages pi citing pa, and visi depends
on the visibilities of the pages citing pi and so on. For n pages this is a system of n
linear equations that has a solution. In praxis, solving this system of equations for some
million pages would be much too expensive (takes to much time), therefore an iterative
approach is used (for the details see (Page et al., 1998).
For even the iterative approach is rather time consuming, the rank (visibility) of all
pages is not computed at query time (when users are waiting for their search results) but
all documents are sorted by visibility offline, and this sorted list is used to fulfill search
requests by selecting the first k documents matching the search term.e
3.2 HITS
The hypertext induced topic selection (HITS) is an alternative approach using the
network structure for document ranking introduced by Kleinberg (Kleinberg, 1999).
Kleinberg identifies two roles a page can fulfill: hub and authority. Hubs are pages
referring to many authorities (e.g. linklists), authorities are pages that are linked by
many hubs. Now for each page pd its hub-value hd and its authority value ad can
be computed:
hdpkCd
ak, adplRd
hl
Here a page pd has a high hd if it links many authorities (i.e. pages pi with
high ai), in other words: it is a good hub, if it knows the important pages. The other
way around pd, has a high ad, if it is linked by many good hubs, i.e. it is listed in the
important link lists. While the authority ad gives the visibility of the document
(visd=ad), hd is only an auxiliary value.
But HITS not only differs from PageRank by the equations used to calculate the
visibility. While the linear equation system is solved iteratively in both algorithms, they
differ in the set of pages used. As PageRank does the calculation offline on all pages
piP and at query time selects the first k matching pages from this presorted list,
HITS in a first step selects all pages matching the search term at query time (this gives a
eFor the technical details see Brin and Page (1998).
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Social Network Models for Enhancing Reference Based Search Engine Rankings
subset M P ) and then adds all pages linked by pages from M ( M
C
piMCi) and
all pages linking pages from M ( M
R
piMRi). Then hd and ad are computed for
all pages pdM'= MCM M R). Only including pages somehow relevant to the
search query (matching pages and neighbors) may improve the quality of the ranking,
but produces high costs at query time for ad and hd are not computed offline because
M
' is dependent on the search term. This is a problem in praxis because a single search
term can have up to some million matching webpages, even if there are strategies for
pre-calculation.f
3.3 Other Approaches
There are other link structure based ranking algorithms, e.g. the hilltop algorithmg
which in a first step identifies so called expert pages (pages linking to a number of
unaffiliated pagesh) and then each page is ranked by the rank of the expert pages it is
linked by, or the TrustRank algorithm (Gyongyi, Garcia-Molina, & Pedersen, 2004),
where link spam is semi-automatically identified by using a small seed of user-rated
pages.
All these algorithms are best suited for networks with cyclic link structures.
Repositories with mainly acyclic reference structure like citation networks of scientific
papers (where each paper cites older ones) or online discussion groups where each
thread consists of one initial posting and sequences of replies/follow-ups, need other
visibility measures, such as those provided by (Malsch et al., 2006) where creation time
of a document is much more important (as scientific papers or postings are not changed
after publishing in contrast to webpages).
fThere is no obvious reason not to use the hub and authority approach for offline calculation on the whole
net. There are search engines claiming to use a HITS-based algorithm, but without giving detailed
information.
gAlso patented by Google.
hUnaffiliated means that they do not belong to the same organization. This is determined by comparing
URLs, IP-addresses and network topology.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
4 Linking and evaluating users
Ranking is not only a phenomenon that takes places in the web but derives from the
archetype stratification of a society into degrees of power and dominance. In principle
in social contexts two types of ranking exist: Explicit (Formal) and Implicit (Informal).
In explicit ranking it is declared who is the one who has the authority to command due
to insignia, symbols or status that he/she is being attributed from the beginning. On the
other side, in implicit ranking it is the opinion and the behaviors of the surrounding
entities that facilitates who has the authority to command and decide.
Both implicit and explicit forms of ranking may exist depending on the purpose of the
social group and the kind of interactions between the members. However our study is
focused only on implicit forms of ranking where status is a social attribute that emerges
rather than being set. In sociology there are several kinds of studies that tend to explain
how status emerges, therefore several research models exist. Those models are analyzed
further in the sections bellow.
4.1 Sociometric Status and Prominence
In a social group one recognizes several strata that characterize their members with a
kind of implicit rank which is known in the social science literature as “status” (Katz,
1953). Usually in a sociological interpretation status denotes power expressed in
different contexts such as political or economical. The most basic theoretical
implication of status is the availability of choices the entity receives in the network
which gives the entity the advantage of negotiation over the others. Depending on the
topology of the network status can be attributed to several nodes of the sociogram (see
figure 3).
In SNA studies, status is depicted upon the generalization of the location of the actor in
the network. In particular status is addressed by how strategic the position of that entity
in a network is (e.g. how it affects the position of the others). Theoretical aspects of
status were defined by Moreno and Jennings (Moreno and Jennings, 1945) as the
instances of the sociometric “star” and “isolate”.
Quantifications of status employ techniques of graph theory such as the centrality index
(Sabidussi, 1966; Freeman, 1979; Friedkin, 1991) which have been adjusted to the
various representations of ties in a network. In our case we summarize the two most
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noteworthy measures of an actor in a directed network which are the “Prestige” and
“Centrality”.
Figure 2: Types of connection degree in the network. It is obvious that the sociometric “star” (first
diagram) is considered the one who has the highest inner degree thus is more popular. When there
is reciprocity the degree of influence can be used to provide an indication of prestige in the
network.
4.1.1 Prestige
A prestige measure is a direct representation of status which often employs the non-
reciprocal connections/choices provided to that entity along with the influence that this
entity might provide to the neighboring entities. In principle Prestige is a non-reciprocal
characteristic of an entity. That means that if the entity is considered prestigious by
another entity it doesn’t mean that this entity will consider the referring entity
prestigious as well.
Inner degree
The simplest measure of prestige is the inner degree index or the popularity of an entity,
which is defined as follows. Considering a graph and the set of the
directed connections between the members of the set V then the
inner degree of a vertex is the sum of the incoming connections to that vertex.
),(: A
EVG =A
E
),......,,( ,2,11,1 ji
eeeE =
in
i
Ki
V
∑
=
=
1
,
jij
in
ieK
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Social Network Models for Enhancing Reference Based Search Engine Rankings
Degree of Influence
However the inner degree index of a vertex makes sense only in cases where a
directional relationship is available (the connection is non-reciprocal) and this cannot be
applied in the study of non directed networks. In that case the prestige is computed by
the influence domain of the vertex. For a non directed graph ),,(: EEVG = the
influence domain of a vertex is the number or proportion of all other vertices which
are connected by a path to that particular vertex.
i
V
∑
=
−
=
1
,
1
1
j
ij
ie
N
d
On the above measure
E
represents the set of paths between the vertices and
and is the number of all available nodes in the graph G (The total number of
nodes
i
Vj
V
1−N
VN = minus the node that is subject to the metric).
Proximity Prestige
A combination of the above two metrics of prestige is known as the “Proximity
Prestige” of vertex encompasses the normalization of the inner degree of the
vertex by its degree of influence such as:
)( i
PP i
V
i
in
i
id
k
PP =
4.1.2 Centrality
Unlike prestige measures which rely mainly on directional relations of the entities the
centrality index of a graph can be calculated in various ways taking also into account
the non-directional connections of the vertex which is examined. The most noteworthy
measures of centrality are classified by the degree of analysis they employ in the graph.
Actor Degree Centrality
Actor degree centrality is the normalized index of the degree of an actor divided by the
maximum number of vertices that exist in a network. Considering a graph
with vertices then the actor degree centrality will be:
),( EVG =n)( id nC
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Social Network Models for Enhancing Reference Based Search Engine Rankings
1
)(
)(
−
=n
nd
nC i
id
Where is the number of the remaining nodes in the graph G. Actor degree
centrality is often interpreted in the literature as the “ego density” (Burt, 1982) of an
actor since it evaluates the importance of the actor based on the ties that connect
him/her to the other members of the network. The highest the actor degree centrality is,
the most prominent this person is in a network, since an actor with a high degree can
potentially directly influence the others.
1−n
Closeness or Distance Centrality
Another measure of centrality the closeness centrality, considers the “geodesic distance”
of a node in a network. For two vertices a geodesic is defined as the length of the
shortest path between them. For a graph ),( EVG
=
the closeness centrality of a
vertex is the sum of geodesic distances between that vertex and all the other vertices
in the network.
)( ic VC
i
V
),(
1
)(
1
n
ii
ic uud
n
VC ∑
=
−
=
Where the function calculates the length of the shortest path between the
vertices i and j and n¡1 is the number of all other vertices in the network. The closeness
centrality can be interpreted as a measurement of the influence of a vertex in a graph:
the higher its value, the easiest it is for that vertex to spread information into that
network. Distance Centrality can be valuable in networks where the actor possesses
transitive properties that through transposition can be spread through direct connections
such as the case of reputation.
),( ni uud
Betweenness Centrality
Betweenness is the most celebrated measure of centrality since not only measures the
prominence of a node based on the position or the activity but also the influence of the
node in information or activity passed to other nodes Considering a graph
with n vertices, the betweenness for vertex
),( EVG =
)(uCBVn
∈
is:
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Social Network Models for Enhancing Reference Based Search Engine Rankings
)2)(1(
)(
)( −−
=∑∈≠≠
nn
u
uC Vtus st
B
σ
Where if and only if the shortest path from s to t passes through v and 0
otherwise. Betweenness can be the basis to interpret roles such as the “gatekeeper” or
the “broker” which are studies extensively in communication networks. A vertex is
considered as a “gatekeeper” if its betweenness and inner degree is relatively high. The
“broker” is a vertex which has relatively high outer degree and betweenness.
1)( =uCB
4.2 Cliques and Cohesive Subgroups
Although the metrics presented above provide an insight to the evaluation of dyadic
relations it’s often necessary to adapt the above measures to the study of larger subset of
entities that share common characteristics such as for instance web pages regarding the
same topic. In network analysis this kind of structure is a subgroup or a clique.
The graph theoretic definition of a clique has its qualitative interpretation in sociometric
research as a discrete social structure (subgroup) shaped and contained within the
structure of a social group (Scott, 2000). The number of cliques contained in a group
and their diameter is subject to the cohesion that characterizes the social structure.
Cohesion depicts how strong the ties between the members of a social group are and
how homogeneous are their properties regarding the overall structure. The highest the
cohesion of a group is then the minimal becomes the size of the cliques (subgroups)
contained and formatted within the group. According to Friedklin (Friedkin, 1998)
cohesion is a factor of influence of the individual by the group standards. A member of
a group is highly connected with the group if and only if he/she accepts and/or
possesses the characteristics of the groups.
The study of cliques can give indications regarding the qualitative aspects of
connections between members of the same structure. For instance, authors of blogs that
provides reference to other blogs that they read and backwards, form a clique of the
general graph of bloggers. Studying how this clique evolves we can model for instance
the spreading of news and blog posts on a perspective of social influence, which can
provide as a way of evaluating the authoritativeness of a specific piece of information
appearing on a website or a blog.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
4.3 The FOAF vocabulary
Although sociometric models can provide several pathways on evaluating the
importance of users, there is a crucial need for an accurate way on mining the relations
between them in order to be able to apply those models. This can be done by using
expressive vocabularies such as FOAF.
The Friend-of-a-Friend vocabulary (Brickley & Miller, 2005) is an expressive
vocabulary set which syntax is based on RDF syntax (Klyne & Carroll, 2004) that is
gaining popularity nowadays as it is used to express the connections between social
entities in the web along with their hypertextual properties such as their homepages or
the emails. In a best case scenario the author of a web page (information resource) will
attach his FOAF profile in the resource in order to make it identifiable as an own
production by the visitors of that page. This can be observed clearly in cases such as
Blogs where the information resource represents the person that expresses his/her views
through the blog. Furthermore connection between blogs represents also a kind of a
directional dichotomous tie between the authors of those blogs.
Vocabulary
Element Description Type of Relational
tie
foaf:knows links foaf:persons Direct
foaf:member Provides affiliation/membership(relates
an entity with a social group)
indirect
foaf:maker Indicates authorship (relates an
information resource with it’s creator)
indirect
foaf:based_near Indicates a spatial affiliation of a social
entity
indirect
foaf:currentproject Indicates a temporal affiliation of a
social entity with a project or an activity
indirect
Table 1: Some representative elements of the FOAF vocabulary and the representation of the
relational tie
In FOAF standard RDF syntax is used to describe the relations between various
acquaintances (relations) of the person described by the FOAF profile. This relation is
depicted in the <foaf:knows> predicate which denotes that the person who has a
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description of <foaf:knows> in his profile for another person, has a social
connection with that person as well. For example a FOAF profile for one of the authors
of this chapter and the connection he has with his coauthors can be described by the
following fragment of RDF code:
<?xml version="1.0" encoding="UTF-8" ?>
<rdf:RDF
xmlns:xml="http://www.w3.org/XML/1998/namespace"
xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#"
xmlns:rdfs=”http://www.w3.org/2000/01/rdf-schema#”>
<foaf:Person rdf:nodeID="friend1">
<foaf:name>Nikolaos Korfiatis</foaf:name>
<foaf:mbox rdf:resource="mailto:nk.inf@cbs.dk"/>
</foaf:Person>
<foaf:Person rdf:nodeID="friend2">
<foaf:name>Claudia Hess</foaf:name>
<foaf:mbox rdf:resource="mailto:claudia.hess@wiai.uni-bamberg.de"/>
</foaf:Person>
<foaf:Person rdf:nodeID="friend3">
<foaf:name>Klauss Stein</foaf:name>
<foaf:mbox rdf:resource="mailto:klaus.stein@wiai.uni-bamberg.de"/>
</foaf:Person>
<foaf:Person rdf:nodeID="me">
<foaf:title>Dr. </foaf:title>
<foaf:givenName>Miguel-Angel</foaf:givenName>
<foaf:family_name>Sicilia</foaf:family_name>
<foaf:mbox rdf:resource="mailto:msicilia@uah.es"/>
<foaf:knows rdf:nodeID="friend1"/>
<rel:collaboratesWith rdf:nodeID="friend2"/>
<rel:collaboratesWith rdf:nodeID="friend3"/>
</foaf:Person>
</rdf:RDF>
Research on descriptions of social relations in the semantic webi is an undergoing effort
which has been initiated lately to address the various concerns and sociological
implications for the expressiveness of social connections in the web. One particular
issue is that although the FOAF vocabulary (see the table 1) has a set of properties for
the description of several kinds of relationships the <foaf:knows> property is the
most common relation that is expressed in a publicly available FOAF profile. According
to the general discussion in the FOAF project the reason for this is that many users
prefer not to express their strength of social connections publicly than to do it with a
general way which the <foaf:knows> property implies.
i See the proceedings on the 1st FOAF Workshop, Galway
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Within well-defined application domains however, more expressive constructs are
required for describing the relationships between persons. Using the social relationships
for recommending sensitive information such as Avesani, Massa & Tiella (Avesani,
Massa & Tiella, 2005) in Moleskiing, a platform for exchanging information on ski
tours (e.g. snow conditions, risk of avalanches), it is highly critical to get these
information only from persons that you consider as trustworthy, and not from everyone
you know (or who is known by some of your friends). Golbeck, Parcia and Hendler
(Golbeck, Garcia & Hendler, 2003) therefore provided an extension to the FOAF
vocabulary so that users can explicitly specify their degree of trust in another person.
Specifying the degree of trust in someone with respect to her or his recommendations of
scientific papers gives according to the trust ontologyj by Golbeck et al. the following:
<foaf:Person rdf:ID="Claudia">
<trust:trustsRegarding>
<trust:TopicalTrust>
<trust:trustSubject
rdf:resource="#RecommendationsForPapers"/>
<trust:trustedPerson rdf:resource="#Klaus"/>
<trust:trustValue>9</trust:trustValue>
</trust:TopicalTrust>
</trust:trustsRegarding>
</foaf:Person>
5 Evaluating web page importance through the analysis of social ties.
Based on the two previous chapters, we present different approaches to integrate the
social information in link-based analysis and the respective algorithms for measuring
the ranking of web pages.
5.1 Integrating imprecise expressions of the social context in PageRank
Garcia and Siclia (Sicilia & Garcia, 2005) have introduced an approach for integrating
the social context of an information resource into the core of ranking algorithms such as
PageRank. Although a flavor of PageRank (PageRank with priors) can be used to
normalize existing rankings the core of the method relies on the expression of the
j The trust ontology is available at http://trust.mindswap.org/ont/trust.owl (last access date 30th of May)
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Social Network Models for Enhancing Reference Based Search Engine Rankings
relational ties of the social context (e.g. connections in a social network) using
imprecise expressions.
The idea is that of using imprecise assessments of social relationships as a weighting
factor for algorithms like PageRank. Whereas imprecise expressions map better to
social relation due to the difficulty of making an accurate estimation of the strength of
the relational ties and their influence to the rest of the structure members.
The model begins by computing a metric called PeopleRank (PPR). PPR is based on the
declared relationships <foaf:knows> connecting pairs of <foaf:Person>
specifications. As aforementioned in section 4.3 the FOAF vocabulary has deliberately
avoided more specific forms of relation like friendship or endorsement, since social
attitudes and conventions on this topic vary greatly between countries and cultures.
In consequence, the strength is provided explicitly as part of the link. In the case of
absence of such value, an “indefinite” middle value is used. With the above, the
PeopleRank can be defined by simply adapting the original PageRank definition:
We assume a social entity/person
A
that has persons which declare they know
him/her (i.e., provide FOAF social pointers to it). The parameter d is a damping factor
which can be set between 0 and 1. [...]. Also C(A) is defined as the number of
(interpreted) declarations of backlinks going out of A's FOAF profile.
n
TT K
1
Following the above definition the PeopleRank of a person
A
( ) can be defined
as follows:
)( APpR
∑
=
+−=
n
ii
i
TC
TPpR
ddAPpR
1)(
)(
)1()(
PageRank PeopleRank
Intuitively, pages that are well cited from
many places around the web are worth
looking at.
Intuitively, the trust on the quality of
pages is related to the degree of
confidence we have on their authors.
Also, pages that have perhaps only one
citation from something like the Yahoo!
home-page are also generally worth
Pages authored or owned by people with a
larger positive prestige should somewhat
be considered more relevant.
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looking at
Table 2: Intuitions behind the development of the PeopleRank algorithm. Adapted from Garcia
and Sicilia (Sicilia & Garcia, 2005).
Even though the idea of ranking by peer’s declarations seems intuitive and is coherent
with the concepts explained in Section 4, the original intuitive justification provided for
PageRank requires a re-formulation. Table 1 provides the original and the socially-
oriented justifications.
An important parameter of the PpR computation is that declarations of social awareness
should be interpreted. Here the management of vagueness plays a role, since there is no
single model or framework that provides a metric for social distance.
The relevance
r
of a social tie in that model is provided by the following general
expression, which provides a value for each edge in the directed graph formed
by the explicitly declared social relationships.
),( 21 ee
)),(()),(()),(( 212121 ppeppSppr ⋅= Asserting that 21 pp
≠
The relevance
r
of an edge is determined from a degree of strength (in a scale of
fuzzy numbers [~0; ~ 10]) weighted by a degree of evidence about the relationship.
These strengths could be provided by extending the current FOAF schema with an
additional attribute.
S
e
Figure 3: The steps of the PpR algorithm.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
Degrees of evidence support a notion of “external” evidence on the relation that
completes the subjectively stated strength. The following expression provides a
formalization for this evidence that is based both on the perceptions of “third parties”
and/or affiliations.
)),(),,(max()),(( 212121 ppPppSppe i
P
Ui∈
Φ= Asserting again that and
having
21 pppi≠≠
Ui ∈
According to the above expression, the strengths of a social tie provided by third parties
in a group of users U are aggregated through simple fuzzy averaging (Φ), and the
evidence provided by work in common projects in which two persons (p1;
p2) collaborate is obtained from FOAF declarations.
i
p
),( 21 ppP
),( 21 ppP
Concretely if we follow the affiliation by the FOAF descriptor e.g. foaf:Project,
people co-working in a project (as declared by <foaf:pastProject>) are credited
an amount of 1, while people that were co-workers (as declared by
<foaf:pastProject>) are credited an amount of 0.1 per common past project.
These somewhat arbitrary values should be complemented by more detailed measures in
the future. Note that “projects" in FOAF are not only formal professional projects but
the concept is open to any informal activity even non-profit or re-creative. Other usage
oriented metrics could be used to complement this approach, such as data obtained from
web usage mining.
Of course the above model can be modified and more parameters could be added.
However but it provides an initial straight forward model for the integration of social
ties in the PageRank from which further empirical analysis could be carried out.
5.2 Integrating trust networks and document reference networks
An alternative approach for integrating different types of networks has been presented
by Hess, Stein & Schlieder (Hess et al., 2006; Stein & Hess, 2005). They propose to
integrate information from a trust network between persons and a document reference
network. A trust network has been chosen as social network because trust relationships
represent a strong basis for recommendations. In contrast to social networks based only
on a ‘knows’ or ‘co-workers’ relationship, trust relationships are more expressive,
although more difficult to obtain. Trust networks cannot automatically be extracted
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from data on the web but users have to indicate their trust relationships as they are
doing it already in an increasing number of trust-based social networks. Examples for
such trust networks are the epinions web of trust in which users rate other users with
respect to the quality of their product reviews, or social networking services such as
Orkut and Linked-in, in which you can rate your friends with respect to their
trustworthiness (among other criteria such as ‘coolness’). In trust networks, trust values
can be inferred for indirectly connected persons by using trust metrics such as those
presented in Goldbeck, Parsia and Hendler (Golbeck, Parsia, & Hendler, 2003) or
Ziegler and Lausen (Ziegler & Lausen, 2004). This reflects the fact that we trust in the
real world the friends of our friends to a certain degree. Trust is therefore not transitive
in the strict mathematical sense but decreases with each additional step in the trust
chain. In contrast to the measures presented in section 4, most trust metrics calculate
highly personalized trust values for the users in the network: a trust value for a user is
always computed from the perspective of a specific user. The evaluations of one and the
same user can therefore greatly vary between users depending on their personal trust
relationships.
We can distinguish two roles that actors play in the trust networks: they can either be
the authors or the reviewers of documents. The term ‘reviewer’ encompasses all persons
having an opinion about the document, such as persons who read the document or
editors who accepted the document for publication. Both cases are addressed in the
following separately, leading to two different trust-enhanced visibility functions.
Case 1: Reviewers & Documents
In this first type of a two-layer-architecture, a trust network between reviewers is
connected with a document reference network. In the document reference network,
documents are linked via citations or hyperlinks. In the second layer, reviewers make
statements about the degree of trust they have in other reviewers in making ‘good’
reviews, i.e. above all that they apply similar criteria to the evaluation as themselves.
Depending on the trust metric that is used for inferring trust values between indirectly
connected persons, trust statements are in [0, 1] with 0 for no trust and 1 for full trust, or
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even [-1, 1] ranging from distrustk to full trust. Both layers are connected via reviews,
i.e. reviewers express their opinion on documents.
We have now two types of information, firstly the reviews of documents made by
persons in which the requesting user has a certain degree of trust, i.e., trust-weighted
reviews, and secondly the visibility of the documents calculated on the basis of the
document network. We now integrate the trust-weighted reviews in the visibility
function and hence personalize it. This new trust-review-weighted-visibility function (in
the following called twr-visibility) has the property that reviews influence the rank of a
document to the degree of trust in the reviewer, i.e. a review by someone deemed as
highly trustworthy influences the rank to a considerable part whereas reviews of less
trustworthy persons have few influence. Having only untrustworthy reviewers, the user
likely prefers a recommendation merely based on the visibility of the documents.
This architecture permits to deal with two interpolation problems. Firstly, propagating
trust values in the trust network makes it possible to consider reviews of users who are
directly linked via a trust statement as well as indirectly via some “friends”, i.e. via
chains of trust. Secondly, reviews influence not only the visibility of the reviewed
papers but also indirectly the visibility of other papers, namely of those papers cited by
the reviewed documents. Figure 4 illustrates these interpolations. Person 1 asks for a
recommendation about document 4 that has not directly been reviewed but which
visibility is influenced by the twr-visibility of document 2 that has been reviewed by a
person deemed as trustworthy by person 1.
k Although distrust statements are difficult to handle in propagation: should I trust someone who is
distrusted by someone I distrust? Or should I distrust this person even more?
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Figure 4- Interpolation in a Document and Reviewer Network
(from Hess, Stein and Schlieder, 2006)
The following function gives the twr-visibility for a document pd in which rd is the
review of document pd and trd is the trust in the reviewer. The trust in the reviewer is
directly taken as the trust in the review.
vf TWR pdtrdrd1trdvf pd
The trust in the reviewers of a document therefore determines to which degree the trust-
weighted reviews influence the twr-visibility. In the case that the trust in the reviewer is
not absolute (trust=1.0), the twr-visibility of the documents citing this document
determines its twr-visiblity. Reviews by trustworthy persons therefore influence
indirectly the twr-visibility of not directly evaluated documents. The vfTWR function
permits to use any visibility function such as the PageRank-formula for the propagation
of the trust-enhanced visibilities. The indirect connections in the trust network are
computed before calculating the twr-visibility. Any trust metric can be used for the
propagation of the trust statements. The above presented function therefore represents a
general framework for integrating trust and document reference networks. A detailed
description of this approach and a simulation demonstrating the personalization
provided by this function is in Hess, Stein and Schlieder (Hess, Stein & Schlieder,
2006).
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Case 2: Authors & Documents
In the second case, the trust network is built up between authors of documents. The trust
statements refer to the trust in the quality and accurateness of the evaluated author’s
documents. As in the case of the reviewer network, the range of the trust values depends
on the trust metric chosen. Authors are connected via an ‘is-author’ relationship with
the documents they have written. Documents can be written by several persons. ‘Is-
author’-edges are not weighted. The structure of the document network is identical as in
the first case: references connect the documents.
This two-layered architecture permits to calculate a trust-weighted visibility for each
document in the document reference network. The idea is that the semantics of links
will differ based on whether there is a trust or a distrust relationship between the citing
and the cited author. This reflects the fact that a link can be set in different contexts. On
the one hand, a link can affirm the authority, the importance or the high quality of a
document, for example the citing author confirms the results of the original work by his
or her own experiments and therefore validates it. On the other, disagreement can be
expressed in a link ranging from different opinions to suspicions that information is
incorrect or even faked.
Several steps are required to compute the trust-weighted visibility:
1. Trust relationships between the authors of a citing and the cited paper are
attributed to the reference between the documents in the document network. In
the case of co-authorship, more than one trust value is available. Assuming that
the opinions of coauthors do not vary extremely, the average of the trust values
is attributed to the reference.
2. The trust values which are attributed to the references have to be transformed
into edge weights, so an edge between two documents pa and pb has a weight
wa b reflecting the relationship (trust) between the authors of the corresponding
papers. This step is necessary as attributed trust values might be negative due to
distrust between the authors. In a visibility function however, we need edge
weights > 0. A mapping function defines how trust values are transferred into
edge weights. Depending on the definition of the mapping function, different
trust semantics can be realized. The mapping function can for instance be
defined in a way that only such references are heavy weighted which express
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Social Network Models for Enhancing Reference Based Search Engine Rankings
high trust whereas another definition could give a high weight to distrust values
in order to get an overview on papers which are not appreciated in a certain
community.
3. The trust-enhanced visibility of the documents can now be calculated by a
visibility function that considers the edge weights. We illustrate this general
framework by using a concrete visibility function for calculating the trust-
weighted visibility, namely the PageRank. The original PageRank formula is
modified such that each page pi contributes not any longer withl visi
Ci to the
visibility visd of another page pd but distributes its visibility according to the
edge weight, so we get
visd1
piRd
wi d
p
j
C
k
wi j
visi
This function can further be personalized so that it calculates individual
recommendations for each user. It can also be adapted such that documents are highly
ranked which are controversially discussed. This approach again is a general
framework. Other visibility functions can be applied instead of the PageRank.
6 Case study: Evaluating Contributions in the Wikipedia
Having presented metrics of social and hypertextual evaluation we choose as a case
study to model the authoritativeness of a system rich of social interactions such as
Wikipedia. Wikipedia is based on Wiki software (Leuf & Cunningham, 2001) and is
considered as one of the most successful collaborative editing projects on the web since
it currently contains over 1 million articlesm and it has an extensive community of
contributors contributing content and improving the quality of the articles.
lwith
C
i being the set of pages cited by
p
i
mStatistics and data for the English Language Wikipedia. For further information about the current size of
the wikipedia the reader can visit : http://en.wikipedia.org/wiki/Wikipedia:Statistics (Last access date
30th of May, 2006)
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Wikipedia is a voluntary project and since it facilitates a large amount of social
connections over a common affiliation we consider it as an interesting example to
discuss authoritativeness over evolving documents. In our case we consider the
following social interactions:
 When a contributor edits content that has been submitted by someone else then it
establishes a tie with him/her. This is depicted by an acceptance factor which
represents the percentage of the content of the previous contributor that is visible
after.
 Every contributor that has a single or more contribution to the article establishes
a relational tie with the other content contributors of the article. Evidence of
participation in common projects strengthens this tie.
As can be seen in Figure 5 we define two different networks the articles network and the
contributors network.
 The Articles Network: Every article in the WIKIPEDIA contains reference to
other articles as well as external references. A set of links used for classification
purposes is also available in most of the active articles of the encyclopaedia.
Every article represents a vertex in the article network and the internal
connections between the articles the edges of the network.
 The Contributors Network: WIKIPEDIA is a collaborative writing effort which
means that an article has multiple contributors. We assume that a contributor
establishes relationship with another contributor if they work on the same
article. In the resulted weighted network each contributor is represented by a
vertex and their social ties (positive or negative) are represented by an edge
denoting the sequence of their social interaction.
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Social Network Models for Enhancing Reference Based Search Engine Rankings
Figure 5: Network layers in the wiki publication model. Contributors are linked together by working on
common projects (articles) in the same topic.
The development of those metrics is based on the following assumptions:
 The more decentralized the editing of an article, then the better this article
represents a consensus about it.
 The contributors whose content has been most accepted (seen from the result of
the diff operation in the wiki) are attributed a level of authority regarding the
article.
 This level of authority remains only in the domain of the article. However,
domains which belong in the same topic can retain the level of authority for a
contributor.
The graph that we model is a signed directed network with arcs signed as a factor
depicting the level of acceptance of the content submitted by contributor A and accepted
by contributor B. In order to model the authoritativeness of contributors, we selected
the degree centrality. In our case study, we use the degree centrality index which is the
simplest definition of centrality and as aforementioned is based on the incoming and
outgoing adjacent connections to other contributors in an article graph. To measure the
centrality at an individual level, we define the contributor degree centrality; and to an
article level, the article degree centralization which represents the variability of
Contributor Degree Centrality over the specific article.
6.1 Contributor Degree Centrality
As have been already discussed the inner degree (in a graph theoretic interpretation: the
amount of edges coming into a node) represents the choices the actor has over a set of
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other actors. However, in our wiki network model the amount of incoming edges
represents edits to the text; therefore the metric of inner degree is the opposite, meaning
that the person with the biggest inner degree has the biggest amount of
objection/rejection in the contributor community and thus receives a kind of negative
evaluation from his/her fellow contributors. On the other hand, the outer-degree of the
vertex represents edits/participation in several parts of the article and thus gives a clue
to the activity of the person in relation to the article and the domain. Mathematically we
can represent such formalism as follows: Considering a graph representing the network
of contributors for an article contributed in the wiki, then the Contributor Degree
Centrality - a contextualized expression of actor degree centrality - is a degree index of
the adjacent connections between the contributor and others who edit the article. From
graph theory, the outer degree of a vertex is the cumulative value of its adjacent
connections.
∑
==
jijiiD xndnC )()(
The adjacent represents the relational tie between the contributors and their
contribution over the domain of the article. This also is characterized by the visibility of
the contribution in the final article and can be either 1 or 0. To provide the centrality, we
divide the degree with the highest obtained degree from the graph which in graph theory
is proved to be the number of remaining vertices (g) minus the ego (g-1). Therefore the
contributor degree centrality can be calculated as an instance of the actor centrality
index:
ij
x
1
)(
)( −
=
′g
nd
nC i
iD
6.2 Article Degree Centralization
We define an Article’s degree centralization as the variability of the individual
contributor centrality indices. The represents the largest observed contributor
degree centrality
DM
C
)( *
nCD
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Social Network Models for Enhancing Reference Based Search Engine Rankings
)2)(1(
)]()([ *
−−
−
=
∑
gg
nCnC
CiD
g
iD
DM
Again we divide the variability with the highest variability observed in the graph in
order to get a normalized calculation.
Cluster
(Outerdegree) Freq Freq% CumFreq CumFreq% Representative
1 1 0.4329 1 0.4329 65.6.92.153
2 199 86.1472 200 86.5801 82.3.32.71
4 14 6.0606 214 92.6407 80.202.248.28
6 6 2.5974 220 95.2381 Snowspinner
8 3 1.2987 223 96.5368 Tim Ivorson
10 1 0.4329 224 96.9697 StirlingNewberry
12 2 0.8658 226 97.8355 24.162.198.123
16 2 0.8658 228 98.7013 JimWae
18 1 0.4329 229 99.1342 Jjshapiro
20 1 0.4329 230 99.5671 SlimVirgin
31 1 0.4329 s 231 100 Amerindianart
Table 3: Contributor Degree Centrality for the wikipedia article “Immanuel Kant”.
6.3 Interpretation of the results
Having defined the metrics, we provide some indicative measurements for a set of
articles from the category philosophy.
Article Name Number of
Contributors Article Degree Centralization
(max 1)
Adam Smith 276 0.039114
Aristotle 274 0.0232
Immanuel Kant 231 0.20484
Johann Wolfgang von Goethe 242 0.016682
John Locke 292 0.008581
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Karl Marx 232 0.006601
Ludwig Wittgenstein 220 0.006328
Philosophy 280 0.00254
Plato 284 0.001207
Socrates 289 0.000405
Table 4: Articles used in the case study along with the number of contributors and their degree
centralization
As can be observed from the table 3, the article degree centralization is relatively low
because of the small collections of articles used in the case study and the inter-
connections of the actors in the domain. However, it is enough to let us discuss some
qualitative interpretations such as:
 The dispersion of the actor indices denotes how dependent this article is on
individual contributors. For instance, if an article has a very low degree of
centralization, then it means that the social process to shape it was highly
distributed, thus resulting in an article which has been submitted by multiple
authorities. In our case, the articles represent a low degree of centralization
which means that contributions have been done by individuals with interests in
other domains as well.
 The range of the group degree centralization reflects the heterogeneity of the
authoring sources of the article. In our case, the article “Immanuel Kant” has a
significantly higher degree of centralization which means that it has been
contributed by authorities most concentrated in the domain of the article and
thus have contributed to other articles.
Contributors with higher inter-relation over the same domain represent higher
authorities based on the assumptions that their interest spans the domain to which the
article belongs and therefore they have conducted background research regarding the
material they have contributed. On the other hand contributors with lesser authority tend
to have their content erased/objected by contributors with higher authority.
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7 Conclusions & Outlook
Current information retrieval models that use links to compute rankings as measures of
the popularity of the Web pages fail to consider the social context that is tacit in the
authorship of the pages and their links. Social Network Analysis provide sound models
for dealing with these kind of models, and can be combined with existing backlink
models to come up with richer models.
This chapter has provided background on backlink models, social network concepts and
their potential application to a combined model of Web retrieval that considers the links
but also the relations between the creators of the documents and the links.
However research issues are open to the direction of applying such models in real
context since the social context is something that is very difficult to extract. The
semantic web can contribute to this direction by advancing the development and use of
vocabularies that can express social relations in various ways and associate them with
content. Nevertheless privacy issues should be also considered since the expression of
social relations in a publicly accessible information space is something that exhibits
vulnerabilities in cases such as “phising” attacks and social engineering (Levy, 2004).
8 Biographical Notes
Nikolaos Korfiatis is a PhD Student in the Department of Informatics at the
Copenhagen Business School (CBS), Denmark. He is currently pursuing his PhD in the
area of recommender systems with a research emphasis on the applications from
behavioral economics and social network theory into the design of more effective
recommender systems. He obtained a BSc in Information Systems from Athens
University of Economics and Business (AUEB), Greece in 2004 and a Master of
Science in Engineering of Interactive Systems from the Royal Institute of Technology
(KTH), Stockholm Sweden in 2006.
Miguel-Ángel Sicilia obtained a Ms.C. degree in Computer Science from the Pontifical
University of Salamanca, Madrid (Spain) in 1996 and a Ph.D. degree from the Carlos III
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Social Network Models for Enhancing Reference Based Search Engine Rankings
University in 2003. He worked as a software architect in e-commerce consulting firms,
being part of the development team of a Web personalization framework at Intelligent
Software Components (iSOCO). From 2002 to 2003 he worked as a full-time lecturer at
the Carlos III University, after which he joined the University of Alcalá. His research
interests are driven by the directions of the Information Engineering Research Unit
which he directs.
Claudia Hess received her diploma in Information Systems from Bamberg University,
Germany in 2004. Currently, she is research assistant at the Laboratory for Semantic
Information Technology at Bamberg University. In her Ph.D. studies, which are jointly
supervised at Bamberg University and University Paris 11, France, she is interested in
the integration of social relationship data, above all trust relationships, into
recommendation and ranking technology.
Klaus Stein obtained an Informatics (CS) diploma in 1998 and a doctoral (PhD) degree
in 2003 from the Munich University of Technology, Germany in the field of spatial
cognition. He currently works on computer-mediated communication processes (COM)
at the Laboratory for Semantic Information Technology at Bamberg University,
Germany.
Christoph Schlieder is Professor and Chair of Computing in the Cultural Sciences
since 2002 at Bamberg University, Germany. He holds a Ph.D. and a Habilitation
degree in computer science, both from the University of Hamburg. Before coming to
Bamberg he was professor of computer science at the University of Bremen where he
headed the Artificial Intelligence Research Group. His primary research interests lie in
developing and applying methods from semantic information processing to problems
from the cultural sciences. Applications areas of his research work include GIS and
mobile assistance technologies as well as digital archives.
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