Type
Conference PaperAuthors
Abdelhamid, EhabCanim, Mustafa
Sadoghi, Mohammad
Bhattacharjee, Bishwaranjan
Chang, Yuan-Chi
Kalnis, Panos

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) DivisionComputer Science Program
King Abdullah University of Science and Technology, , United States
Date
2018-10-25Online Publication Date
2018-10-25Print Publication Date
2018-04Permanent link to this record
http://hdl.handle.net/10754/630372
Metadata
Show full item recordAbstract
Frequent subgraph mining is a core graph operation used in many domains. Most existing techniques target static graphs. However, modern applications utilize large evolving graphs. Mining these graphs using existing techniques is infeasible because of the high computational cost. We propose IncGM+, a fast incremental approach for frequent subgraph mining on large evolving graphs. 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 efficiency, IncGM+ stores a number of selected embeddings to avoid redundant expensive subgraph isomorphism operations. Moreover, the proposed system supports batch updates. Our results confirm that IncGM+ outperforms existing methods, scales to larger graphs and consumes less memory.Citation
Abdelhamid E, Canim M, Sadoghi M, Bhattacharjee B, Chang Y-C, et al. (2018) Incremental Frequent Subgraph Mining on Large Evolving Graphs. 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00241.Conference/Event name
34th IEEE International Conference on Data Engineering, ICDE 2018Additional Links
https://ieeexplore.ieee.org/document/8509462ae974a485f413a2113503eed53cd6c53
10.1109/ICDE.2018.00241