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
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
ACM SIGKDD Explorations Newsletter
The dynamics of viral marketing
ACM Transactions on the Web (TWEB)
Graph clustering with network structure indices
Proceedings of the 24th international conference on Machine learning
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Computing communities in large networks using random walks
ISCIS'05 Proceedings of the 20th international conference on Computer and Information Sciences
Formal Concept Analysis: foundations and applications
Formal Concept Analysis: foundations and applications
Social-Based Conceptual Links: Conceptual Analysis Applied to Social Networks
ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
From Frequent Features to Frequent Social Links
International Journal of Information System Modeling and Design
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Numerous social network mining methods have been proposed until now for addressing social mining tasks and specially searching for communities or frequent social patterns. However, the degree of complementarity of these methods has been very little studied. In this paper, we focus on two knowledge extraction processes in social networks: a link-based clustering that may extract social communities and a recent approach that searches for frequent conceptual link involving both clustering and search for frequent social patterns. We explore how the models extracted by each method may match and which potential useful knowledge they may provide. Our objective is to evaluate the potential relationships between communities and frequent conceptual links. For this purpose, we propose a set of measures for evaluating the degree to which these patterns extracted from the same dataset are matching. Our approach is applied on two datasets and demonstrates the importance of considering simultaneously various kinds of knowledge and their complementarity.