Type
ArticleAuthors
Abdelhamid, Ehab
Canim, Mustafa
Sadoghi, Mohammad
Bhatta, Bishwaranjan
Chang, Yuan-Chi
Kalnis, Panos

KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Date
2017-08-22Online Publication Date
2017-08-22Print Publication Date
2017-12-01Permanent link to this record
http://hdl.handle.net/10754/625837
Metadata
Show full item recordAbstract
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 problem 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.Citation
Abdelhamid E, Canim M, Sadoghi M, Bhatta B, Chang Y-C, et al. (2017) Incremental Frequent Subgraph Mining on Large Evolving Graphs. IEEE Transactions on Knowledge and Data Engineering: 1–1. Available: http://dx.doi.org/10.1109/TKDE.2017.2743075.Additional Links
http://ieeexplore.ieee.org/document/8014497/ae974a485f413a2113503eed53cd6c53
10.1109/TKDE.2017.2743075