Issue No. 12 - Dec. (2017 vol. 29)
Ehab Abdelhamid , Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
Mustafa Canim , IBM Thomas J. Watson Research Center, Yorktown Heights, NY
Mohammad Sadoghi , Computer Science Department, University of California, Davis, Davis, CA
Bishwaranjan Bhattacharjee , IBM Thomas J. Watson Research Center, Yorktown Heights, NY
Yuan-Chi Chang , IBM Thomas J. Watson Research Center, Yorktown Heights, NY
Panos Kalnis , Computer, Electrical and Mathematical Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia
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.
Indexes, Data mining, Graphical models, Memory management, Itemsets, Computer security
E. Abdelhamid, M. Canim, M. Sadoghi, B. Bhattacharjee, Y. Chang and P. Kalnis, "Incremental Frequent Subgraph Mining on Large Evolving Graphs," in IEEE Transactions on Knowledge & Data Engineering, vol. 29, no. 12, pp. 2710-2723, 2017.