Algorithmics and applications of tree and graph searching
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
SPIN: mining maximal frequent subgraphs from graph databases
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Substructure similarity search in graph databases
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
CLAN: An Algorithm for Mining Closed Cliques from Large Dense Graph Databases
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Searching Substructures with Superimposed Distance
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
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This paper proposes an efficient method for indexing and mining graph database. Most existing approaches are based on frequent sub-structures such as edges, paths, or subgraphs. However, as the size of graphs increases, such index structure grows drastically in size for avoiding performance degradation. This yields a requirement for constructing a more compact index structure and introducing more informative indexing items into this index to increase its pruning power. In this paper, we demonstrate that degree information can help solve this problem. Based on this idea, we propose a new index structure (D-index) which uses the subgraph and its degree information as the indexing item. Our empirical study shows that D-index achieves remarkable improvement in performance over the state-of-the-art approach.