Discovering Patterns in Social Networks with Graph Matching Algorithms

Ogaard, Kirk; Roy, Heather; Kase, Sue E.; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

doi:10.1007/978-3-642-37210-0_37

Cited by 14 publications

(4 citation statements)

References 3 publications

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“…Given a pattern query Q and a graph G, graph pattern matching is to find the set Q(G) of matches of Q in G. It is used in, e.g., social marketing, knowledge discovery, mobile network analysis, intelligence analysis for identifying terrorist organizations [25], and the study of adolescent drug use [17].…”

Section: Introductionmentioning

confidence: 99%

Making pattern queries bounded in big graphs

Cao

Fan

Huai

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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It is cost-prohibitive to find matches Q(G) of a pattern query Q in a big graph G. We approach this by fetching a small subgraph GQ of G such that Q(GQ) = Q(G). We show that many practical patterns are effectively bounded under access constraints A commonly found in real life, such that GQ can be identified in time determined by Q and A only, independent of the size |G| of G. This holds no matter whether pattern queries are localized (e.g., via subgraph isomorphism) or non-localized (graph simulation). We provide algorithms to decide whether a pattern Q is effectively bounded, and if so, to generate a query plan that computes Q(G) by accessing GQ, in time independent of |G|. When Q is not effectively bounded, we give an algorithm to extend access constraints and make Q bounded in G. Using real-life data, we experimentally verify the effectiveness of the approach, e.g., about 60% of queries are effectively bounded for subgraph isomorphism, and for such queries our approach outperforms the conventional methods by 4 orders of magnitude.

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Section: Introductionmentioning

confidence: 99%

Making pattern queries bounded in big graphs

Cao

Fan

Huai

et al. 2015

2015 IEEE 31st International Conference on Data Engineering

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“…Such drawbacks have led the research community to investigate other models of GPM that are less expensive in time and space and exploit better the semantic information held by labels. Graph simulation and its extensions are considered nowadays the best fit techniques for GPM in many of the emerging applications like analyzing associations in social networks [58].…”

Section: Exploration-based Approachesmentioning

confidence: 99%

A Survey on Distributed Graph Pattern Matching in Massive Graphs

et al. 2021

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Besides its NP-completeness, the strict constraints of subgraph isomorphism are making it impractical for graph pattern matching (GPM) in the context of big data. As a result, relaxed GPM models have emerged as they yield interesting results in a polynomial time. However, massive graphs generated by mostly social networks require a distributed storing and processing of the data over multiple machines, thus, requiring GPM to be revised by adopting new paradigms of big graphs processing, e.g., Think-Like-A-Vertex and its derivatives. This article discusses and proposes a classification of distributed GPM approaches with a narrow focus on the relaxed models.

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“…Firstly, in [20] a software called Graph Matching Toolkit (gmt) is presented. It allows to graphically construct graph patterns for their search.…”

Section: Graph Pattern Matchingmentioning

confidence: 99%

Expert system for crop disease based on graph pattern matching: a proposal

Sambony

Corrales

2016

rev.ing.univ.Medellin

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For agroindustry, crop diseases constitute one of the most common problems that generate great economic losses and low production quality. On the other hand, from the field of computer science, several tools have emerged that may improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used to store different types of variables present in a crop environment, allowing the application of graph data mining techniques like graph pattern matching. In this paper, we present an overview of the above issues and the proposal of an expert system for crop disease based on graph pattern matching.

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Discovering Patterns in Social Networks with Graph Matching Algorithms

Cited by 14 publications

References 3 publications

Making pattern queries bounded in big graphs

Making pattern queries bounded in big graphs

A Survey on Distributed Graph Pattern Matching in Massive Graphs

Expert system for crop disease based on graph pattern matching: a proposal

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