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b) and c) 1-hop and 2-hop neighborhood of the gray node extracted from the graph in a)
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The influence of the network's structure on the dynamics of spreading processes has been extensively studied in the last decade. Important results that partially answer this question show a weak connection between the macroscopic behavior of these processes and specific structural properties in the network, such as the largest eigenvalue of a topol...
Citations
... Focusing on a node as spreading source, we infer that the number of links in its downwind area gives a first estimate of the potential for a release in the node to affect many other locations in the network. To delimit this downwind area, we adopt the concept of n-hop neighborhood 32,33 . Two nodes are n hops apart if it is possible to reach the target node from the source node by traveling n links. ...
The incidental or malicious release of toxic gases in the atmosphere is one of the most critical scenarios for cities. The impact of these releases varies with the ventilation potential of the urban environment. To disentangle this crucial aspect, vulnerability to airborne releases is here traced back to essential properties of the urban fabric. To this aim, pollutant dispersion is disassembled in its fundamental bricks and the main drivers of the process are captured. The analysis is based on four cities with emblematic architectures: Paris, Firenze, Lyon and New York. Results show that vulnerability is driven by the topology of the city and by its interaction with the approaching wind. In this sense, fragility to toxic releases is written in the layout of the urban fabric and results from its historical evolution. This study paves the way to the assessment of air pollution-related issues from a morphological point of view.
... Dynamical processes on complex networks have growing interest because of their wide scope of applications. Epidemic spreading in populations [16], influence spreading in social networks [17] and the flow of traffic on roads are important practical applications [15]. In some specific applications, it is not clear which kind of models best describe the system, or whether both static and dynamic behaviour can coexist in the same system [18]. ...
... Spreading processes on networks cover a variety of situations because processes can depend on states of other nodes or links in the network. For example, virus spreading may not be possible or it is only partial in case nodes are immunised as a result of previous contamination [16]. Another example is when information or rumours are spread more actively when heard for the first time compared to later versions of the same information. ...
... Influence spreading models are designed for describing complex social interactions in social network structure [16,28]. These interactions propagate via connections, or paths, between people. ...
We present methods for analysing hierarchical and overlapping community structure and spreading phenomena on complex networks. Different models can be developed for describing static connectivity or dynamical processes on a network topology. In this study, classical network connectivity and influence spreading models are used as examples for network models. Analysis of results is based on a probability matrix describing interactions between all pairs of nodes in the network. One popular research area has been detecting communities and their structure in complex networks. The community detection method of this study is based on optimising a quality function calculated from the probability matrix. The same method is proposed for detecting underlying groups of nodes that are building blocks of different subcommunities in the network structure. We present different quantitative measures for comparing and ranking solutions of the community detection algorithm. These measures describe properties of sub-communities: strength of a community, probability of formation and robustness of composition. The main contribution of this study is proposing a common methodology for analysing network structure and dynamics on complex networks. We illustrate the community detection methods with two small network topologies. In the case of network spreading models, time development of spreading in the network can be studied. Two different temporal spreading distributions demonstrate the methods with three real-world social networks of different sizes. The Poisson distribution describes a random response time and the e-mail forwarding distribution describes a process of receiving and forwarding messages.
... Focusing on a node as spreading source, we infer that the number of links in its downwind area gives a first estimate of the potential for a release in the node to affect many other locations in the network. We delimit this downwind area of the source node as its n-hop neighborhood [Smilkov andKocarev, 2012, Maglaras andKatsaros, 2012], i.e. as the subnetwork composed by the nodes that are reachable from the source via at most n hops along the directed links (see Section 1.1). We propose the number of links in this neighborhood (r) as a suitable measure of reachability from the node. ...
Air pollution in urban areas is a major concern for the health and safety of citizens. Hubs of economic, political and social activities, cities are densely populated
and are exposed to a large number of pollution sources. These factors, together
with the risk of malicious acts for terrorist purposes, make them extremely vulnerable to gaseous releases. To predict and manage air pollution scenarios, the
understanding and modelling of dispersion phenomena in the urban atmosphere is
crucial. Differently from transport processes far from boundaries, flow and dispersion in the urban canopy are primarily governed by the intricate geometry of the
city. The aim of this thesis is to investigate and model the effect of urban form
on the dispersion of pollutants at two different spatial scales: at the scale of the
district and at the scale of the single street canyon.
Urban street pattern is the dominant geometry at the district scale. Starting
from this observation, the first part of this thesis proposes a new perspective based
on the theory of complex networks to analyze and model the transport of pollutants along the streets of a city. The urban canopy is modelled as a network. Street
canyons and their intersections shape the spatial structure of the network. The direction and the transport capacity of the flow in the streets define the direction and
the weight of the links. Adopting this mathematical interpretation, propagation is
modelled as a spreading process on a network and the most dangerous areas in
a city are identified as the best spreading nodes. To this aim, a novel centrality
metric tailored to mass transport in flow networks is derived. Besides providing
an operational tool to identify the places in a city with the highest potential for
dispersion over large areas, the proposed approach is suitable to investigate which
structural properties of a city make it fragile to air pollution. The comparison
between four emblematic cities with different urban patterns evidences that vulnerability is driven by the topological properties of the urban fabric, i.e. street
connectivity and the variability in the orientation of the streets with respect to the
approaching wind.
At the scale of the single street, flow and dispersion dynamics are governed by
the canyon geometry. The dispersion of pollutants can be decomposed in the longitudinal transport along the street and the vertical transfer between the canyon and
the external flow. While advection drives the dynamics along the canyon axis, the mechanisms of mass exchange in the vertical direction are still not fully understood.
The second part of this thesis investigates these processes by means of wind tunnel
experiments of pollutant dispersion in a canyon oriented perpendicular to the wind
direction. In this configuration, the vertical exchange is the dominant mechanism
of canyon ventilation. Keeping the external flow unaltered, we analyse the effect
of different boundary conditions at the building walls and the presence of obstacles
within the canyon. The boundary conditions are modified by alternatively heating
the downwind and upwind walls of the canyon, by changing its aspect ratio and by
introducing roughness elements at walls. Two rows of model trees are arranged at
the sides of a street canyon to simulate urban vegetation. Velocity and concentration measurements are performed within the canyon and a characteristic exchange
velocity between the street canyon and the overlying atmosphere is estimated to
quantify the overall canyon ventilation in the different configurations. Results evidence that the efficiency of the vertical exchange between the canyon and external
flow is mainly driven by the fluctuating component of the turbulent flow within the
canyon. The intensity and spatial distribution of the turbulent kinetic energy field
varies according to the geometry of the canyon, the conditions imposed at the walls
and the presence of obstacles.
In short, this thesis contributes to our understanding of (i) the role of urban
geometry in the dispersion of pollutants, and (ii) the physical mechanisms that
govern urban ventilation. Moreover, the techniques and methods adopted in this
study highlight the importance of a multi-scale approach and the potential of innovative tools, both conceptual and experimental, to develop operational models for
the assessment of urban air pollution.
... Another study demonstrated how the network topology and bursting slowed down the spreading [19]. In [28], the influence of the network topology on epidemic spreading was shown. The effects of network topology were also analysed for rumour spreading [25]. ...
Social networks create routes for information transmission with the use of social influence mechanisms. While one of goals can be to increase the dynamics of processes within the networks, from the perspective of the spread of harmful content or misleading information, efforts should be focused on limiting the spread. One possible strategy is the initialisation of competing processes as a reaction to negative spread. Depending on the parameters of the limiting process and the network topologies, actions can succeed or fail. In this paper, we focused on a linear threshold competing model and analysed the effects of the limiting processes within scale-free networks with different characteristics, as well as the process parameters and timing. The results showed how the parameters of the limiting process should be adjusted to achieve substantial coverage within the network depending on the network characteristics and the intervals between the positive and negative processes.
... Another important factor is the structure of bands or demes into larger regional metapopulations. Network topology, for example, is known to have a substantial effect upon contagion or diffusion processes (e.g., Castellano et al., 2009;Smilkov and Kocarev, 2012). Thus, it is likely that regional structure has critical effects on the outcomes we can expect from a single social "learning rule. ...
Cultural transmission has long been a key organizing principle within anthropology, but the effort to formalize cultural transmission models and fit them to archaeological data is more recent, stimulated by work by Robert Dunnell in the 1970’s. Since then, the use of cultural transmission modeling in archaeology has branched into several research programs: one macroevolutionary, employing phylogenetic methods; and one microevolutionary, employing models derived from population genetics. A third research program, focused on intermediate or “mesoscopic” scales and seriation as a finer-grained counterpart to phylogenetic and cladistics, is being developed by Carl Lipo and the present author.
This dissertation collects research papers by the author since 2012 which examine two questions. First, are equifinality issues encountered in the microevolutionary research program solvable or do they prevent us from employing individual-scale models? Second, to the extent that equifinality cannot be circumvented, can we construct better approaches at the mesosopic scale appropriate to coarse grained, time averaged data?
Two papers examine the first question, using simulation modeling and statistical methods to test whether theoretical models can be distinguished even in principle. The first paper examines the effects of temporal aggregation, which is ubiquitous in the archaeological record, on our ability to distinguish between cultural transmission models, and finds significant issues in doing so with time averaged data. The second paper examines the effects of population heterogeneity in social learning modes, which is well documented from living human and animal populations. I find that heterogeneous mixtures of social learning rules can be identified statistically, but only with synchronic censusing of the population, and that time averaging and small samples render mixtures indistinguishable from pure unbiased copying.
Turning to the second question, three papers continue my long-term research into reshaping the classical seriation method into a tool for tracing the structure of cultural transmision at regional scales. One short paper examines the combinatorial structure of the seriation problem when we admit multiple subsolutions. A second paper seeks to increase the size of possible seriations, which is necessary to incorporate significant spatial variation and yield a tool usable for investigating the history of cultural transmission in a region. We increase the potential size of seriation solutions by switching from unimodality to distance minimization as the ordering criterion, yielding “continuity” seriation as a distinct method. A third paper in this group then applies continuity seriation graphs as the observable variable, in a methodological study of how to construct models of how cultural transmission was structured at the regional scale. This paper introduces ``interval temporal networks’’ as a way to formalize our hypotheses about regional interaction and transmission, and explores a statistical method for summarizing the topology of seriation graphs, to assess their fit to our regional interaction models.
A final paper examines a different kind of mesoscale question: how do we begin to model not just the spatiotemporal structure of past cultural transmission, but its as well. The chapter models the dependency structure of the knowledge required to construct complex artifact types, through the “prerequisites” needed for each step, and introduces a model where transmission of subsequent traits requires learning their prerequisites first. This simplified model of “structured” cultural traits is then used to explore the “learning hypothesis” for behavioral modernity, by looking at the richness and depth of knowledge gained when transmission is mostly accomplished by simple imitation compared to learning via a teacher. The results are suggestive that the learning hypothesis can account for the increased richness of “behaviorally modern” hominids, and more importantly, points the way to more substantive and technologically informed cultural transmission models.
... These connections are the most likely to be activated in the real-world social networks. Bk is also an appropriate choice because it models a location-based mesh-like topology, which is a critical aspect of epidemics spreading [40]. ...
The infectious diseases are spreading due to human interactions enabled by various social networks. Therefore, when a new pathogen such as SARS-CoV-2 causes an outbreak, the non-pharmaceutical isolation strategies (e.g., social distancing) are the only possible response to disrupt its spreading. To this end, we introduce the new epidemic model (SICARS) and compare the centralized (C), decentralized (D), and combined (C+D) social distancing strategies, and analyze their efficiency to control the dynamics of COVID-19 on heterogeneous complex networks. Our analysis shows that the centralized social distancing is necessary to minimize the pandemic spreading. The decentralized strategy is insufficient when used alone, but offers the best results when combined with the centralized one. Indeed, the (C+D) is the most efficient isolation strategy at mitigating the network superspreaders and reducing the highest node degrees to less than 10% of their initial values. Our results also indicate that stronger social distancing, e.g., cutting 75% of social ties, can reduce the outbreak by 75% for the C isolation, by 33% for the D isolation, and by 87% for the (C+D) isolation strategy. Finally, we study the impact of proactive versus reactive isolation strategies, as well as their delayed enforcement. We find that the reactive response to the pandemic is less efficient, and delaying the adoption of isolation measures by over one month (since the outbreak onset in a region) can have alarming effects; thus, our study contributes to an understanding of the COVID-19 pandemic both in space and time. We believe our investigations have a high social relevance as they provide insights into understanding how different degrees of social distancing can reduce the peak infection ratio substantially; this can make the COVID-19 pandemic easier to understand and control over an extended period of time.
... The seminal works in this area considered how spreading dynamics are shaped by the structural properties of stylized networks such as small-world, characterized by the property of high clustering and short average path lengths between any two nodes [17], and scale-free, characterized by the heterogeneous distribution of connectivity to other nodes [18,19]. The influence of network structural properties such as degree heterogeneity, betweenness, degree density, weight distribution, and others on outbreak spreading characteristics have since been studied extensively over the past decade [20,21,22,23]. More recently, this approach has been applied in the context of commodity or livestock trade with the aim of assessing how network topology mediates the dynamics and size of an outbreak of deliberate or unintentional contamination. ...
... since this is the case when both information types are able to spread in the network. The cases when only one information type has transmission to forgetting ratio above the threshold reduce to a SIS epidemic model, and for this model the question of determining bounds on the fixed point has been solved [29]. ...
... • We find that when a node i has high degree, the probability of receiving information of any type from any combination of its infective neighbours tends to 1, and the probabilities to adopt information type 1 or 2 are well approximated by Future research directions are numerous. One can apply the methodology of [29] to obtain better bounds on the probabilities of infection for the case when the degree of the nodes is not very high for the aforementioned approximations to hold. Comparing the model predictions to real data is a key question to its usefulness. ...
A b s t r a c t: In this article we propose a model for the spread of two types of information in networks. The model is a natural generalization of the epidemic susceptible-infective-susceptible(SIS) model. The two information types have different attractiveness, which affects the nodes' decision on which information type to adopt when both arrive at a node in the same time step. At difference with results from other authors, the model shows simultaneous existence of the two information types in the stable state. We give approximations for the average number of nodes informed with each information type at the end of the spreading process when nodes have high degree.
... We calculated 24 the Node Betweenness Centrality (NBC) and the PageRank (PR) of each node as measures of the importance of these taxa in the network. In parasitic networks, host NBC is related to Scientific RepoRts | 5:10361 | DOi: 10.1038/srep10361 the number of parasites infecting a host 24 and to the parasite's host range and transmission ability at the level of the entire network 25 . The PR assigns a universal rank to nodes based on the importance of the other nodes to which it is linked. ...
Natural foci of ticks, pathogens, and vertebrate reservoirs display complex relationships that are key
to the circulation of pathogens and infection dynamics through the landscape. However, knowledge
of the interaction networks involved in transmission of tick-borne pathogens are limited because
empirical studies are commonly incomplete or performed at small spatial scales. Here, we applied
the methodology of ecological networks to quantify >14,000 interactions among ticks, vertebrates,
and pathogens in the western Palearctic. These natural networks are highly structured, modular,
coherent, and nested to some degree. We found that the large number of vertebrates in the network
contributes to its robustness and persistence. Its structure reduces interspecific competition and
allows ample but modular circulation of transmitted pathogens among vertebrates. Accounting for
domesticated hosts collapses the network’s modular structure, linking groups of hosts that were
previously unconnected and increasing the circulation of pathogens. This framework indicates that
ticks and vertebrates interact along the shared environmental gradient, while pathogens are linked
to groups of phylogenetically close reservoirs.
... Collective behavior often emerges from sporadic interactions between individuals in a contact network. Such dynamic behavior consequently forms large complex diffusion networks in the real world such as the Internet [45,56,139,176,187], online social networks [11,66,94,105,115,122], email networks [77,116,129,148,160], mobile phone networks [18,54,71,96,131], collaboration networks [27,34,67,101,125,184], cortical networks [16,55,112,150,164], metabolic and protein networks [28,36,58,73,74], and so on. Thus, analyzing the linkage patterns of real-world complex networks is a meaningful first step towards a systematic understanding of the underlying diffusion mechanisms. ...
Real diffusion networks are complex and dynamic, since underlying social structures
are not only far-reaching beyond a single homogeneous system but also frequently
changing with the context of diffusion. Thus, studying topic-related diffusion across
multiple social systems is important for a better understanding of such realistic situations.
Accordingly, this thesis focuses on uncovering topic-related diffusion dynamics
across heterogeneous social networks in both model-driven and model-free ways.
We first conduct empirical studies for analyzing diffusion phenomena in real
world systems, such as new diffusion in social media and knowledge transfer in
academic publications. We observe that large diffusion is more likely attributed to
interactions between heterogeneous social networks as if they were in the same networks.
Thus, external influences from out-of-the-network sources, as observed in
previous work, need to be explained with the context of interactions between heterogeneous
social networks. This observation motivates our new conceptual framework
for cross-population diffusion, which extends the traditional diffusion mechanism to
a more flexible and general one.
Second, we propose both model-driven and model-free approaches to estimate global
trends of information diffusion. Based on our conceptual framework, we propose a
model-driven approach which allows internal influence to reach heterogeneous populations
in a probabilistic way. This approach extends a simple and robust mass action
diffusion model by incorporating the structural connectivity and heterogeneity
of real-world networks. We then propose a model-free approach using informationtheoretic
measures with the consideration of both time-delay and memory effects
on diffusion. In contrast to the model-driven approach, this model-free approach
does not require any assumptions on dynamic social interactions in the real world,
providing the benefits of quantifying nonlinear dynamics of complex systems.
Finally, we compare our model-driven and model-free approaches in accordance
with different context of diffusion. This helps us to obtain a more comprehensive understanding
of topic-related diffusion patterns. Both approaches provide a coherent
macroscopic view of global diffusion in terms of the strength and directionality of
influences among heterogeneous social networks. We find that the two approaches
provide similar results but with different perspectives, which in conjunction can help
better explain diffusion than either approach alone. They also suggest alternative options
as either or both of the approaches can be used appropriate to the real situations
of different application domains.
We expect that our proposed approaches provide ways to quantify and understand
cross-population diffusion trends at a macro level. Also, they can be applied
to a wide range of research areas such as social science, marketing, and even neuroscience,
for estimating dynamic influences among target regions or systems.
