GRAMI: Frequent subgraph and pattern mining in a single large graph

M. Elseidy, Ehab Abdelhamid, S. Skiadopoulos, Panos Kalnis

Research output: Contribution to journalArticlepeer-review

239 Scopus citations


Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or proteinprotein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GRAMI, a novel framework for frequent subgraph mining in a single large graph. GRAMI undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GRAMI that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGRAMI, a version supporting structural and semantic constraints, and AGRAMI, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches. 2014 VLDB Endowment.
Original languageEnglish (US)
Pages (from-to)517-528
Number of pages12
JournalProceedings of the VLDB Endowment
Issue number7
StatePublished - May 12 2015

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KAUST Repository Item: Exported on 2020-10-01


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