An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
ACM SIGKDD Explorations Newsletter
Conceptual and Spatial Footprints for Complex Systems Analysis: Application to the Semantic Web
DEXA '09 Proceedings of the 20th International Conference on Database and Expert Systems Applications
Analyzing Social Networks Using FCA: Complexity Aspects
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Identifying and evaluating community structure in complex networks
Pattern Recognition Letters
Formal Concept Analysis: foundations and applications
Formal Concept Analysis: foundations and applications
Mining attribute-structure correlated patterns in large attributed graphs
Proceedings of the VLDB Endowment
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In this work, we propose a novel approach for the discovery of frequent patterns in a social network on the basis of both vertex attributes and link frequency. With an analogy to the traditional task of mining frequent item sets, we show that the issue addressed can be formulated in terms of a conceptual analysis that elicits conceptual links. A social-based conceptual link is a synthetic representation of a set of links between groups of vertexes that share similar internal properties. We propose a first algorithm that optimizes the search into the concept lattice of conceptual links and extracts maximal frequent conceptual links. We study the performances of our solution and give experimental results obtained on a sample example. Finally we show that the set of conceptual links extracted provides a conceptual view of the social network.