A Survey on Distributed Graph Pattern Matching in Massive Graphs
Sarra Bouhenni, Saïd Yahiaoui, Nadia Nouali‐Taboudjemat, Hamamache Kheddouci
Abstract
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.
Topics & Concepts
Computer scienceGraph isomorphismTheoretical computer scienceBig dataVertex (graph theory)Matching (statistics)Pattern matchingGraphTime complexityArtificial intelligenceAlgorithmData miningMathematicsLine graphStatisticsGraph Theory and AlgorithmsAdvanced Graph Neural NetworksData Management and Algorithms