Discovering groups of people in Google news
Proceedings of the 1st ACM international workshop on Human-centered multimedia
A Taxonomy Learning Method and Its Application to Characterize a Scientific Web Community
IEEE Transactions on Knowledge and Data Engineering
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
A New Content-Based Model for Social Network Analysis
ICSC '08 Proceedings of the 2008 IEEE International Conference on Semantic Computing
Topic and role discovery in social networks
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
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Relationships among actors in traditional social network analysis are modelled as a function of the quantity of relations (co-authorships, business relations, friendship, etc.). In contrast, within a business, social or research community, network analysts are interested in the communicative content exchanged by the community members, not merely in the number of relationships. In order to meet this need, this paper presents a novel social network model, in which the actors are not simply represented through the intensity of their mutual relationships, but also through the analysis and evolution of their shared interests. Text mining and clustering techniques are used to capture the content of communication and to identify the most popular topics.