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    Incremental Frequent Subgraph Mining on Large Evolving Graphs

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    08014497.pdf
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    Type
    Article
    Authors
    Abdelhamid, Ehab cc
    Canim, Mustafa
    Sadoghi, Mohammad
    Bhatta, Bishwaranjan
    Chang, Yuan-Chi
    Kalnis, Panos cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2017-08-22
    Online Publication Date
    2017-08-22
    Print Publication Date
    2017-12-01
    Permanent link to this record
    http://hdl.handle.net/10754/625837
    
    Metadata
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    Abstract
    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.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    IEEE Transactions on Knowledge and Data Engineering
    DOI
    10.1109/TKDE.2017.2743075
    Additional Links
    http://ieeexplore.ieee.org/document/8014497/
    ae974a485f413a2113503eed53cd6c53
    10.1109/TKDE.2017.2743075
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

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