Graph indexing: a frequent structure-based approach
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Closure-Tree: An Index Structure for Graph Queries
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Fg-index: towards verification-free query processing on graph databases
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
iGraph: a framework for comparisons of disk-based graph indexing techniques
Proceedings of the VLDB Endowment
CT-index: Fingerprint-based graph indexing combining cycles and trees
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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Graphs are popular data structures for modeling complex data types. There is a need for managing such graph data and providing efficient querying tools. In the graph mining realm, the problem lies in indexing a large number of graphs for fast retrieval. Indexing attributed graphs and using attributed queries can provide faster response time and results that are more refined. Our index technique ECTree focuses on extending an existing index to support attributed graph indexing and providing subgraph querying access to the extended index. The aim is to find a way such that the labels of the graphs as well as the attributes of the graphs are indexed at the same time. A query format is provided to query the extended index with flexibility on the attributes. In addition, regular expressions are used as query labels to provide flexibility. We also introduce a label-irrelevant vertex degree-attribute pruning method. All the techniques presented in our work are validated through experiments on both real and synthetic datasets.