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QGRAPH is a new visual language for querying and updating graph databases. In QGRAPH the user can draw a query consisting of some vertices and edges with specified relations between their attributes. The response will be the collection of all subgraphs of the database that have the desired pattern. QGRAPH is very useful for knowledge discovery. QGR...
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... we want to find remake, original¡ pairs such that one of the two movies received an Oscar for Best Picture while the other did not. This query can be succinctly expressed with an undirected RemakeOf edge between the two Movie vertices (Figure 8). The silent classic Ben-Hur (1925) and the 1959 remake starring Charlton Heston match this query. ...
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... To enable non-technical domain experts to successfully interrogate a large biomedical knowledge graph, an interactive and visual pattern-building interface provides an intuitive means for users to encode their questions and thoughts fluidly, demonstrated by Blau et al. (2002), Chau et al. (2008) and Pienta et al. (2016). The visual pattern being created also provides a common language between experts from different backgrounds -particularly biology and technology -resulting in a collaborative environment that helps fuse drug discovery expertise with data modeling expertise, to maximise retrieval of the most promising results. ...
... The p-value is adjusted for the false discovery rate using the Benjamini and Hochberg procedure (Benjamini, 2010). ...
The onset of the 2019 Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic necessitated the identification of approved drugs to treat the disease, before the development, approval and widespread administration of suitable vaccines. To identify such a drug, we used a visual analytics workflow where computational tools applied over an AI-enhanced biomedical knowledge graph were combined with human expertise. The workflow comprised rapid augmentation of knowledge graph information from recent literature using machine learning (ML) based extraction, with human-guided iterative queries of the graph. Using this workflow, we identified the rheumatoid arthritis drug baricitinib as both an antiviral and anti-inflammatory therapy. The effectiveness of baricitinib was substantiated by the recent publication of the data from the ACTT-2 randomised Phase 3 trial, followed by emergency approval for use by the FDA, and a report from the CoV-BARRIER trial confirming significant reductions in mortality with baricitinib compared to standard of care. Such methods that iteratively combine computational tools with human expertise hold promise for the identification of treatments for rare and neglected diseases and, beyond drug repurposing, in areas of biological research where relevant data may be lacking or hidden in the mass of available biomedical literature.
... Example 1 Inspired from [19], Fig. 1 depicts a data graph G that represents some Oscar-winning movies. Each node in G is depicted with its label (Person, Movie, Oscar) and, for more clarification, with an attribute (i.e. ...
... When using strong simulation [23] however, all movies on G are returned. This is due to the fact that strong simulation, as all extensions of graph simulation, does not support the notion of inequality constraint [19] (called also Label-Repetition Constraints [26]) over nodes of the graph patterns. ...
... One can adopt an isomorphism-based graph pattern matching model [15,16,19] to specify and match expressive graph patterns, as those of previous example. However, the more expressive are graph patterns, the more cost-prohibitive are the isomorphism-based models (as observed in [15] for QGraph). ...
In recent years, we have witnessed an increasing use of graph pattern matching in a wide variety of applications such as social networks analysis, knowledge discovery, software plagiarism detection and many more. It is typically defined in terms of subgraph isomorphism, an NP-Complete problem. To overcome this cost, many extensions of graph simulation have been proposed that allow graph pattern matching to be conducted in cubic-time. However, in emerging applications, more expressive patterns are needed, notably ones with counting quantifiers (CQs) which are not considered by simulation-based approaches. In this article, we propose a simulation-based graph pattern matching approach that supports CQs on edges of graph patterns. We first consider CQs that express numeric aggregates only. We show that our approach is in ptime as earlier extensions of graph simulation by providing a cubic-time quantified matching algorithm, i.e., an algorithm for matching graph patterns that contain CQs. In the second part, we discuss the problem of Label-Repetition Constraints (LRCs). We define a necessary and sufficient condition for the satisfaction of LRCs. Based on this condition, we give an extension of our quantified matching algorithm to deal with LRCs in ptime, together with an optimization technique. Finally, we show that our quantified graph pattern matching approach retains the same complexity bounds when dealing with ratio aggregates. To our knowledge, this is the first effort to deal with numeric aggregates, ratio aggregates, and LRCs on graph patterns in ptime.
... 3. Effect of autocorrelation Figure 8 and 9 shows the performance of model in different autocorrelation scenarios. Second order model performs better 7 Related vertices may contains all nearest neighbours connected in GD 8 Qgraph, used by Proximity to make graph queries visual and efficient [11] in low to moderate autocorrelation and comparable in high autocorrelation environment. In high autocorrelation scenarios prediction is biased because same variable of related objects give enough information to predict the attribute. ...
... Commonly, users formulate queries by building graph patterns, as in [1,6,11,15,17,24,36], including filter conditions with logical and other types of operations such as those for managing incomplete data (e.g. optional in SPARQL-based languages), or set operations (e.g. ...
End-users who are experts in an application domain need to retrieve data from databases in order to answer particular inquiries. Visual Query Systems (VQSs) facilitate this task; but, formulating complex queries is still challenging for end-users. In particular, VQSs for graph databases can be broadly classified into two groups: One group is based on data exploration and allow for direct manipulation of data graphs, but with limited query expressiveness. The other group, oriented to build visual sentences that represent the query, provide greater expressiveness, but they transfer complex language concepts and operations to the visual language. Thus, this research developed GraphTQL a visual query system that provides greater expressiveness and ease in the query formulation. GraphTQL uses a graph data model to depict the domain of interest at the conceptual level. A set of operators provides the means to transform schema and instance graphs, reducing them progressively to obtain the data of interest. The operators include implicit management of incomplete data; as well as clarification dialogues to guide users in order to express complex filter conditions and to select the paths which connect entities in the graph. The development of GraphTQL followed a user-centered design approach which focused on the medical domain. Thus, potential users’ contribution and feedback played a significant role in this work. A comparison with a graphical query interface for SPARQL showed better results for GraphTQL regarding the number of queries correctly formulated by users, the number of errors and the time spent for query formulation.
... Other papers address the problem of defining visual query languages for graph data sets. Notable examples are the system GRAPHITE [23] and the system PROXIMITY, based on the query language of Blau et al. [24]. A system developed by Koenig et al. [25] returns a set of candidate results approximating a desired pattern and which allows for a subsequent interaction with these results in order to refine them. ...
This paper describes TAXNET, a decision support system for tax evasion discovery, based on a powerful visual language and on advanced network visualization techniques. It has been developed in cooperation with the Italian Revenue Agency, where it is currently used. TAXNET allows users to visually define classes of suspicious patterns, it exploits effective graph pattern matching technologies to rapidly extract subgraphs that correspond to one or more patterns, it provides facilities to conveniently merge the results, and it implements new ad-hoc centrality indexes to rank taxpayers based on their fiscal risk. Moreover, it offers a visual interface to analyze and interact with those networks that match a desired pattern. The paper discusses the results of an experimental study and some use cases conducted with expert officers on real data and in a real working environment. The experiments give evidence of the effectiveness of our system.
... Rules for social recommendation are studied in [LAR00], using support count as constraints. QGRAPH [BIJ02] annotates nodes and edges with a counting range (count 0 as negated edge) to specify the number of matches that must exist in a database. Set regular path queries (SRPQ) [LSZD13] extends regular path queries with quantification for group selection, to restrict the nodes in one set connected to the nodes of another. ...
... (3) Quantified matching with QGPs is DP-complete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [Pap03]. In contrast, SPARQL and SPARQLog are PSPACE-hard [LLP10], and SRPQ takes EXPTIME [LSZD13]; while the complexity bounds for QGRAPH [BIJ02], SocialScope [AYLY09] and SNQL [SMGW11] are unknown, they are either more expensive than QGPs, (e.g., QGRAPH is a fragment of FO(count)), or cannot express numeric and ratio quantifiers [AYLY09,SMGW11]. (4) No prior work has studied parallel scalable algorithms for its queries. ...
The need for graph computations is evident in a multitude of use cases. To support computations on large-scale graphs, several parallel systems have been developed. However, existing graph systems require users to recast algorithms into new models, which makes parallel graph computations as a privilege to experienced users only. Moreover, real world applications often require much more complex graph processing workflows than previously evaluated. In response to these challenges, the thesis presents GRAPE, a distributed graph computation system, shipped with various applications for social network analysis, social media marketing and functional dependencies on graphs. Firstly, the thesis presents the foundation of GRAPE. The principled approach of GRAPE is based on partial evaluation and incremental computation. Sequential graph algorithms can be plugged into GRAPE with minor changes, and get parallelized as a whole. The termination and correctness are guaranteed under a monotonic condition. Secondly, as an application on GRAPE, the thesis proposes graph-pattern association rules (GPARs) for social media marketing. GPARs help users discover regularities between entities in social graphs and identify potential customers by exploring social influence. The thesis studies the problem of discovering top-k diversified GPARs and the problem of identifying potential customers with GPARs. Although both are NP- hard, parallel scalable algorithms on GRAPE are developed, which guarantee a polynomial speedup over sequential algorithms with the increase of processors. Thirdly, the thesis proposes quantified graph patterns (QGPs), an extension of graph patterns by supporting simple counting quantifiers on edges. QGPs naturally express universal and existential quantification, numeric and ratio aggregates, as well as negation. The thesis proves that the matching problem of QGPs remains NP-complete in the absence of negation, and is DP-complete for general QGPs. In addition, the thesis introduces quantified graph association rules defined with QGPs, to identify potential customers in social media marketing. Finally, to address the issue of data consistency, the thesis proposes a class of functional dependencies for graphs, referred to as GFDs. GFDs capture both attribute-value dependencies and topological structures of entities. The satisfiability and implication problems for GFDs are studied and proved to be coNP-complete and NP-complete, respectively. The thesis also proves that the validation problem for GFDs is coNP- complete. The parallel algorithms developed on GRAPE verify that GFDs provide an effective approach to detecting inconsistencies in knowledge and social graphs.
... Rules for social recommendation are studied in [98], using support count as constraints. QGRAPH [74] annotates nodes and edges with a counting range (count 0 as negated edge) to specify the number of matches that must exist in a database. Set regular path queries (SRPQ) [50] extends regular path queries with quantification for group selection, to restrict the nodes in one set connected to the nodes of another. ...
... (3) We show that quantified matching is DPcomplete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [102]. In contrast, SPARQL and SPARQLog are PSPACE-hard [97], and SRPQ takes EXPTIME [50]; while the complexity bounds for QGRAPH [74], SocialScope [3] and SNQL [53] are unknown, they are either more expensive than quantified patterns (e.g., QGRAPH is a fragment of FOðcountÞ) or cannot express numeric and ratio quantifiers [3,53]. ...
This position paper provides an overview of our recent advances in the study of big graphs, from theory to systems to applications. We introduce a theory of bounded evaluability, to query big graphs by accessing a bounded amount of the data. Based on this, we propose a framework to query big graphs with constrained resources. Beyond queries, we propose functional dependencies for graphs, to detect inconsistencies in knowledge bases and catch spams in social networks. As an example application of big graph analyses, we extend association rules from itemsets to graphs for social media marketing. We also identify open problems in connection with querying, cleaning and mining big graphs.
... Rules for social recommendation are studied in [30], using support count as constraints. QGRAPH [10] annotates nodes and edges with a counting range (count 0 as negated edge) to specify the number of matches that must exist in a database. Set regular path queries (SRPQ) [31] extends regular path queries with quantification for group selection, to restrict the nodes in one set connected to the nodes of another. ...
... (3) Quantified matching with QGPs is DP-complete at worst, slightly higher than conventional matching (NP-complete) in the polynomial hierarchy [33]. In contrast, SPARQL and SPARQLog are PSPACE-hard [28], and SRPQ takes EXPTIME [31]; while the complexity bounds for QGRAPH [10], SocialScope [5] and SNQL [32] are unknown, they are either more expensive than QGPs (e.g., QGRAPH is a fragment of FO(count)), or cannot express numeric and ratio quantifiers [5,32]. (4) No prior work has studied parallel scalable algorithms for its queries. ...
This paper proposes quantified graph patterns (QGPs), an extension of graph patterns by supporting simple counting quantifiers on edges. We show that QGPs naturally express universal and existential quantification, numeric and ratio aggregates, as well as negation. Better still, the increased expressivity does not come with a much higher price. We show that quantified matching, i.e., graph pattern matching with QGPs, remains NP-complete in the absence of negation, and is DP-complete for general QGPs. We show how quantified matching can be conducted by incorporating quantifier checking into conventional subgraph isomorphism methods. We also develop parallel scalable algorithms for quantified matching. As an application of QGPs, we introduce quantified graph association rules defined with QGPs, to identify potential customers in social media marketing. Using real-life and synthetic graphs, we experimentally verify the effectiveness of QGPs and the scalability of our algorithms.
... However, this system has several limitations: (i) it is not designed to directly work on top of widely used graph database technologies; (ii) its visual language is quite simple and does not allow for the creation of sophisticated patterns; (iii) the interaction of the user with the presented results is rather limited. Conversely, QGraph is a more complete visual query language for graphs [3]. It is used in the knowledge discovery system Proximity (https://kdl.cs.umass.edu/display/public/Proximity), ...
We present Kojaph, a new system for the visual definition and exploration of patterns in graph databases. It offers an expressive visual language integrated in a simple user interface, to define complex patterns as a combination of topological properties and node/edge attribute properties. Users can also interact with the query results and visually explore the graph incrementally, starting from such results. From the application perspective, Kojaph has been designed to run on top of every desired graph database management system (GDBMS). As a proof of concept, we integrated it with Neo4J, the most popular GDBMS.
... The requirements discussed above demand representations more elaborate than the simple graphs (or matrices). This challenge is not exclusive of the SN&M and it has been explored extensively in other contexts (Guting 1994;Blau et al. 2002;Angles and Gutierrez 2008). From this background and trends in information exchange indicating that all the information should be in the same data structure and that it should support the addition of arbitrary metadata (e.g., provenance), it follows that the data structure that represents SN&M should have at least the following characteristics: Fig. 4 Social media example. ...
