Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A vector space model for automatic indexing
Communications of the ACM
Cultivating Communities of Practice: A Guide to Managing Knowledge
Cultivating Communities of Practice: A Guide to Managing Knowledge
The Journal of Machine Learning Research
Topic and role discovery in social networks with experiments on enron and academic email
Journal of Artificial Intelligence Research
Virtual Communities of Practice's Purpose Evolution Analysis Using a Concept-Based Mining Approach
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Topic-based social network analysis for virtual communities of interests in the dark web
ACM SIGKDD Explorations Newsletter
Leveraging social network analysis with topic models and the Semantic Web extended
Web Intelligence and Agent Systems - Web Intelligence and Communities
A new dissimilarity measure for online social networks moderation
Web Intelligence and Agent Systems - Web Intelligence and Communities
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The identification of communities in social networks is a common problem that researchers have been dealing using network analysis properties. However, in environments where community members are connected by digital documents, most researchers have either emphasize to solve the community discovery problem computing structural properties of networks, ignoring the underlying semantic information from digital documents. In this paper, we propose a novel approach to combine traditional network analysis methods for community detection with text mining techniques. This way, extracted communities can be labeled according to latent semantic information within documents, called topics. Our proposal was evaluated in Plexilandia, a virtual community of practice with more than 2,500 members and 9 years of commentaries.