Incremental frequent subgraph mining on large evolving graphs

Ehab Abdelhamid*, Mustafa Canim, Mohammad Sadoghi, Bishwaranjan Bhattacharjee, Yuan Chi Chang, Panos Kalnis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

47 Scopus citations


Frequent subgraph mining is a core graph operation used in many domains, such as graph data management and knowledge exploration, bioinformatics, and security. Most existing techniques target static graphs. However, modern applications, such as social networks, utilize large evolving graphs. Mining these graphs using existing techniques is infeasible, due to the high computational cost. In this paper, we propose IncGM+, a fast incremental approach for continuous frequent subgraph mining on a single large evolving graph. We adapt the notion of 'fringe' to the graph context, that is the set of subgraphs on the border between frequent and infrequent subgraphs. IncGM+ maintains fringe subgraphs and exploits them to prune the search space. To boost the efficiency, we propose an efficient index structure to maintain selected embeddings with minimal memory overhead. These embeddings are utilized to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Using large real-world graphs, we experimentally verify that IncGM+ outperforms existing methods by up to three orders of magnitude, scales to much larger graphs and consumes less memory.

Original languageEnglish (US)
Article number8014497
Pages (from-to)2710-2723
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Issue number12
StatePublished - Dec 1 2017

Bibliographical note

Publisher Copyright:
© 1989-2012 IEEE.


  • Data mining
  • Graph algorithms
  • Indexing

ASJC Scopus subject areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics


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