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Diversified topk graph pattern matching
 PVLDB
"... 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 ex ..."
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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 reallife 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 topk 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 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 topk matches of uo based on r(), with the early termination property, i.e., they find topk matches without computing the entireM(Q;G). (4) We also study diversified topk matching, a bicriteria optimization problem based on both r() and d(). We show that its decision problem is NPcomplete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using reallife and synthetic data, we experimentally verify that our (diversified) topk matching algorithms are effective, and outperform traditional matching algorithms in efficiency. 1.
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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 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 reallife social graphs. Moreover, inpracticemanysocialqueriesaretofindmatches of a specific pattern node, rather than the entire M(Q,G). This paper studies topk 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 topk matches of uo based on δr(), with the early termination property, i.e., they find topk matches without computing the entire M(Q,G). (4) We also study diversified topk matching, a bicriteria optimization problem based on both δr() and δd(). We show that its decision problem is NPcomplete. Nonetheless, we provide an approximation algorithm with performance guarantees and a heuristic one with the early termination property. (5) Using reallife and synthetic data, we experimentally verify that our (diversified) topk matching algorithms are effective, and outperform traditional matching algorithms in efficiency. 1.
A Survey: Static and Dynamic Ranking
"... The search engines are an important source of information. They work on mechanism of information retrieval. But the task does not end here. The bulk information retrieved has to be provided to the user as a list such that the best suited information lies at the top and so on. This process is called ..."
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The search engines are an important source of information. They work on mechanism of information retrieval. But the task does not end here. The bulk information retrieved has to be provided to the user as a list such that the best suited information lies at the top and so on. This process is called ranking. This paper is a review on different ranking algorithms broadly classified into static and dynamic ranking techniques.
Truth Discovery under Resource Constraints
, 2015
"... work contained in this document has been submitted in support of an application for a degree or qualification of this or any other university or other institution of learning. All verbatim extracts have been distinguished by quotation marks, and all sources of information have been specifically ackn ..."
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work contained in this document has been submitted in support of an application for a degree or qualification of this or any other university or other institution of learning. All verbatim extracts have been distinguished by quotation marks, and all sources of information have been specifically acknowledged. Signed: Date: March 27, 2015 Social computing initiatives that mark a shift from personal computing towards computations involving collective action, are driving a dramatic evolution in modern decisionmaking. Decisionmakers or stakeholders can now tap into the power of tremendous numbers and varieties of information sources (crowds), capable of providing information for decisions that could impact individual or collective wellbeing. More information sources does not necessarily translate to better information quality, however. Social influence in online environments, for example, may bias collective opinions. In addition, querying information sources may be costly, in terms of energy, bandwidth, delay overheads, etc., in realworld applications.
Submodular Point Processes with Applications to Machine Learning
"... We introduce a class of discrete point processes that we call the Submodular Point Processes (SPPs). These processes are characterized via a submodular (or supermodular) function, and naturally model notions of information, coverage and diversity, as well as cooperation. Unlike Logsubmodular an ..."
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We introduce a class of discrete point processes that we call the Submodular Point Processes (SPPs). These processes are characterized via a submodular (or supermodular) function, and naturally model notions of information, coverage and diversity, as well as cooperation. Unlike Logsubmodular and Logsupermodular distributions (LogSPPs) such as determinantal point processes (DPPs), SPPs are themselves submodular (or supermodular). In this paper, we analyze the computational complexity of probabilistic inference in SPPs. We show that computing the partition function for SPPs (and LogSPPs), requires exponential complexity in the worst case, and also provide algorithms which approximate SPPs up to polynomial factors. Moreover, for several subclasses of interesting submodular functions that occur in applications, we show how we can provide efficient closed form expressions for the partition functions, and thereby marginals and conditional distributions. We also show how SPPs are closed under mixtures, thus enabling maximum likelihood based strategies for learning mixtures of submodular functions. Finally, we argue how SPPs complement existing LogSPP distributions, and are a natural model for several applications. 1