Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Social Networks for Targeted Advertising
HICSS '06 Proceedings of the 39th Annual Hawaii International Conference on System Sciences - Volume 06
ACM SIGKDD Explorations Newsletter
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Annual Review of Information Science and Technology
The Gaston Tool for Frequent Subgraph Mining
Electronic Notes in Theoretical Computer Science (ENTCS)
Diffusion in dynamic social networks: application in epidemiology
DEXA'11 Proceedings of the 22nd international conference on Database and expert systems applications - Volume Part II
From Frequent Features to Frequent Social Links
International Journal of Information System Modeling and Design
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In the network modeling area, the most widely used definition of a "pattern" is that of a subgraph, a notion that considers only the network topological structure. While this definition has been very useful for extracting subgraphs frequently found in a network or a set of networks, it does not take into account the node attributes, an intrinsic component of social networks that often provides relevant information on the role or the position of a node in a network. In this paper, we propose a novel approach for extracting frequent patterns in social networks, called frequent link mining, based on the search for particular patterns that combine information on both network structure and node attributes. This kind of patterns, that we call frequent links, provides knowledge on the groups of nodes connected in the social network. In this article, we detail the method proposed for extracting frequent links and discuss its flexibility and its complexity. We demonstrate the efficiency of our solution by carrying out qualitative and quantitative studies.