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Diversified top-k graph pattern matching
- PVLDB
"... Graph pattern matching has been widely used in e.g., so-cial data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q;G) of matches of Q in G. How-ever, these algorithms often return an excessive number of matches, and are ex ..."
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Cited by 7 (1 self)
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Graph pattern matching has been widely used in e.g., so-cial data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q;G) of matches of Q in G. How-ever, these algorithms often return an excessive number of matches, and are expensive on large real-life social graphs. Moreover, in practice many social queries are to find matches of a specific pattern node, rather than the entire M(Q;G). This paper studies top-k graph pattern matching. (1) We revise graph pattern matching defined in terms of simula-tion, by supporting a designated output node uo. Given G and Q, it is to find those nodes in M(Q;G) that match uo, instead of the large setM(Q;G). (2) We study two classes of functions for ranking the matches: relevance functions r() based on, e.g., social impact, and distance functions d() to cover diverse elements. (3) We develop two algorithms for computing top-k matches of uo based on r(), with the early termination property, i.e., they find top-k matches without computing the entireM(Q;G). (4) We also study diversified top-k matching, a bi-criteria optimization problem based on both r() and d(). We show that its decision problem is NP-complete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using real-life and synthetic data, we experimentally verify that our (diversi-fied) top-k matching algorithms are effective, and outper-form traditional matching algorithms in efficiency. 1.
Expert recommendation based on social drivers, social network analysis, and semantic data representation
- In Proceedings of the 2nd International Workshop on Information Heterogeneity and Fusion in Recommender Systems (HetRec '11). ACM
, 2011
"... ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous ..."
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ABSTRACT Knowledge networks and recommender systems are especially important for expert finding within organizations and scientific communities. Useful recommendation of experts, however, is not an easy task for many reasons: It requires reasoning about multiple complex networks from heterogeneous sources (such as collaboration networks of individuals, article citation networks, and concept networks) and depends significantly on the needs of individuals in seeking recommendations. Although over the past decade much effort has gone into developing techniques to increase and evaluate the quality of recommendations, personalizing recommendations according to individuals' motivations has not received much attention. While previous work in the literature has focused primarily on identifying experts, our focus here is on personalizing the selection of an expert through a principled application of social science theories to model the user's motivation. In this paper, we present an expert recommender system capable of applying multiple theoretical mechanisms to the problem of personalized recommendations through profiling users' motivations and their relations. To this end, we use the Multi-Theoretical Multi-Level (MTML) framework which investigates social drivers for network formation in the communities with diverse goals. This framework serves as the theoretical basis for mapping motivations to the appropriate domain data, heuristic, and objective functions for the personalized expert recommendation. As a proof of concept, we developed a prototype recommender grounded in social science theories, and utilizing computational techniques from social network analysis and representational techniques from the semantic web to facilitate combining and operating on data from heterogeneous sources. We evaluated the prototype's ability to predict collaborations for scientific research teams, using a simple off-line methodology. Preliminary results demonstrate encouraging success while offering significant personalization options and providing flexibility in customizing the recommendation heuristic based on users' motivations. In particular, recommendation heuristics based on different motivation profiles result in different recommendations, and taken as a whole better capture the diversity of observed expert collaboration.
Answering Graph Pattern Queries Using Views
"... Abstract—Answering queries using views has proven an effec-tive technique for querying relational and semistructured data. This paper investigates this issue for graph pattern queries based on (bounded) simulation, which have been increasingly used in, e.g., social network analysis. We propose a not ..."
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Cited by 4 (3 self)
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Abstract—Answering queries using views has proven an effec-tive technique for querying relational and semistructured data. This paper investigates this issue for graph pattern queries based on (bounded) simulation, which have been increasingly used in, e.g., social network analysis. We propose a notion of pattern containment to characterize graph pattern matching using graph pattern views. We show that a graph pattern query can be answered using a set of views if and only if the query is contained in the views. Based on this characterization we develop efficient algorithms to answer graph pattern queries. In addition, we identify three problems associated with graph pattern contain-ment. We show that these problems range from quadratic-time to NP-complete, and provide efficient algorithms for containment checking (approximation when the problem is intractable). Using real-life data and synthetic data, we experimentally verify that these methods are able to efficiently answer graph pattern queries on large social graphs, by using views. I.
Who is the Barbecue King of Texas?: ⇤ A Geo-Spatial Approach to Finding Local Experts on Twitter
"... This paper addresses the problem of identifying local experts in so-cial media systems like Twitter. Local experts – in contrast to gen-eral topic experts – have specialized knowledge focused around a particular location, and are important for many applications includ-ing answering local information ..."
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This paper addresses the problem of identifying local experts in so-cial media systems like Twitter. Local experts – in contrast to gen-eral topic experts – have specialized knowledge focused around a particular location, and are important for many applications includ-ing answering local information needs and interacting with com-munity experts. And yet identifying these experts is difficult. Hence in this paper, we propose a geo-spatial-driven approach for identify-ing local experts that leverages the fine-grained GPS coordinates of millions of Twitter users. We propose a local expertise framework that integrates both users ’ topical expertise and their local authority. Concretely, we estimate a user’s local authority via a novel spatial proximity expertise approach that leverages over 15 million geo-tagged Twitter lists. We estimate a user’s topical expertise based on expertise propagation over 600 million geo-tagged social con-nections on Twitter. We evaluate the proposed approach across 56 queries coupled with over 11,000 individual judgments from Ama-zon Mechanical Turk. We find significant improvement over both general (non-local) expert approaches and comparable local expert finding approaches.
ExpFinder: Finding Experts by Graph Pattern Matching
"... Abstract—We present ExpFinder, a system for finding experts in social networks based on graph pattern matching. We demon-strate (1) how ExpFinder identifies top-K experts in a social network by supporting bounded simulation of graph patterns, and by ranking the matches based on a metric for social i ..."
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Abstract—We present ExpFinder, a system for finding experts in social networks based on graph pattern matching. We demon-strate (1) how ExpFinder identifies top-K experts in a social network by supporting bounded simulation of graph patterns, and by ranking the matches based on a metric for social impact; (2) how it copes with the sheer size of real-life social graphs by supporting incremental query evaluation and query preserving graph compression, and (3) how the GUI of ExpFinder interacts with users to help them construct queries and inspect matches. I.
unknown title
"... Graph pattern matching has been widely used in e.g., social data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q,G) of matches of Q in G. However, these algorithms often return an excessive number of matches, and are expe ..."
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Graph pattern matching has been widely used in e.g., social data analysis. A number of matching algorithms have been developed that, given a graph pattern Q and a graph G, compute the set M(Q,G) of matches of Q in G. However, these algorithms often return an excessive number of matches, and are expensive on large real-life social graphs. Moreover, inpracticemanysocialqueriesaretofindmatches of a specific pattern node, rather than the entire M(Q,G). This paper studies top-k graph pattern matching. (1) We revise graph pattern matching defined in terms of simulation, by supporting a designated output node uo. Given G and Q, it is to find those nodes in M(Q,G) that match uo, instead of thelarge set M(Q,G). (2) Westudy twoclasses of functions for ranking the matches: relevance functions δr() based on, e.g., social impact, and distance functions δd() to cover diverse elements. (3) We develop two algorithms for computing top-k matches of uo based on δr(), with the early termination property, i.e., they find top-k matches without computing the entire M(Q,G). (4) We also study diversified top-k matching, a bi-criteria optimization problem based on both δr() and δd(). We show that its decision problem is NP-complete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using real-life and synthetic data, we experimentally verify that our (diversified) top-k matching algorithms are effective, and outperform traditional matching algorithms in efficiency. 1.
Learning Graphic Design Skills on the Web: Challenges in Locating, Understanding, and Employing External Help
"... When people engaged in graphic design tasks have difficulty solving problems by themselves, they turn to external help resources such as the web, help documents, or nearby colleagues. The web has expanded the amount of diversity of help options available, both when they learn on their own and when t ..."
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When people engaged in graphic design tasks have difficulty solving problems by themselves, they turn to external help resources such as the web, help documents, or nearby colleagues. The web has expanded the amount of diversity of help options available, both when they learn on their own and when they learn with others. We present findings from two laboratory studies designed to better understand learning challenges in locating, understanding, and employing external help in the context of graphic design tasks. The first study investigates learning on one’s own by searching the web for information. We find that participants struggled to formulate accurate queries, failed to recognize appropriate webpages, and had difficulty in transferring knowledge from the web content to the task at hand. The second study focuses on learning with others by connecting with remote teachers for synchronous help. We observe that these teacher-learner pairs faced difficulties in building and maintaining shared context, and in managing the cost of synchronous social interaction. The findings contribute to a comprehensive understanding of learning with online resources for graphic design tasks, and suggest opportunities for better learning environments. Author Keywords graphic design, online learning, web search, synchronous help
10.1109/TKDE.2015.2429138, IEEE Transactions on Knowledge and Data Engineering 1 Answering Pattern Queries Using Views
"... Abstract—Answering queries using views has proven effective for querying relational and semistructured data. This paper investigates this issue for graph pattern queries based on graph simulation. We propose a notion of pattern containment to characterize graph pattern matching using graph pattern v ..."
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Abstract—Answering queries using views has proven effective for querying relational and semistructured data. This paper investigates this issue for graph pattern queries based on graph simulation. We propose a notion of pattern containment to characterize graph pattern matching using graph pattern views. We show that a pattern query can be answered using a set of views if and only if it is contained in the views. Based on this characterization, we develop efficient algorithms to answer graph pattern queries. We also study problems for determin-ing (minimal, minimum) containment of pattern queries. We establish their complexity (from cubic-time to NP-complete) and provide efficient checking algorithms (approximation when the problem is intractable). In addition, when a pattern query is not contained in the views, we study maximally contained rewriting to find approximate answers; we show that it is in cubic-time to compute such rewriting, and present a rewriting algorithm. We experimentally verify that these methods are able to efficiently answer pattern queries on large real-world graphs. 1
