On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
G-hash: towards fast kernel-based similarity search in large graph databases
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
APPT '09 Proceedings of the 8th International Symposium on Advanced Parallel Processing Technologies
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Diagnosing memory leaks using graph mining on heap dumps
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate weighted frequent pattern mining with/without noisy environments
Knowledge-Based Systems
Mining frequent subgraphs to extract communication patterns in data-centres
ICDCN'11 Proceedings of the 12th international conference on Distributed computing and networking
Discovering highly reliable subgraphs in uncertain graphs
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Frequent approximate subgraphs as features for graph-based image classification
Knowledge-Based Systems
Approximate graph mining with label costs
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Recently, there arise a large number of graphs with massive sizes and complex structures in many new applications, such as biological networks, social networks, and the Web, demanding powerful data mining methods. Due to inherent noise or data diversity, it is crucial to address the issue of approximation, if one wants to mine patterns that are potentially interesting with tolerable variations. In this paper, we investigate the problem of mining frequent approximate patterns from a massive network and propose a method called gApprox. gApprox not only finds approximate network patterns, which is the key for many knowledge discovery applications on structural data, but also enriches the library of graph mining methodologies by introducing several novel techniques such as: (1) a complete and redundancy-free strategy to explore the new pattern space faced by gApprox; and (2) transform "frequent in an approximate sense" into an anti-monotonic constraint so that it can be pushed deep into the mining process. Systematic empirical studies on both real and synthetic data sets show that frequent approximate patterns mined from the worm protein-protein interaction network are biologically interesting and gApprox is both effective and efficient.