Complete Mining of Frequent Patterns from Graphs: Mining Graph Data
Machine Learning
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
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Exact and Approximate Graph Matching Using Random Walks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
ORIGAMI: A Novel and Effective Approach for Mining Representative Orthogonal Graph Patterns
Statistical Analysis and Data Mining
RAM: Randomized Approximate Graph Mining
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
gApprox: Mining Frequent Approximate Patterns from a Massive Network
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
TALE: A Tool for Approximate Large Graph Matching
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Output space sampling for graph patterns
Proceedings of the VLDB Endowment
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
An efficient graph-mining method for complicated and noisy data with real-world applications
Knowledge and Information Systems - Special Issue on "Context-Aware Data Mining (CADM)"
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Many real-world graphs have complex labels on the nodes and edges. Mining only exact patterns yields limited insights, since it may be hard to find exact matches. However, in many domains it is relatively easy to define a cost (or distance) between different labels. Using this information, it becomes possible to mine a much richer set of approximate subgraph patterns, which preserve the topology but allow bounded label mismatches. We present novel and scalable methods to efficiently solve the approximate isomorphism problem. We show that approximate mining yields interesting patterns in several real-world graphs ranging from IT and protein interaction networks to protein structures.