Cluster ranking with an application to mining mailbox networks
Knowledge and Information Systems
An Algorithm to Find Overlapping Community Structure in Networks
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Cluestr: mobile social networking for enhanced group communication
Proceedings of the ACM 2009 international conference on Supporting group work
Inferring relevant social networks from interpersonal communication
Proceedings of the 19th international conference on World wide web
Suggesting friends using the implicit social graph
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Expert Systems with Applications: An International Journal
Sentic Computing: Techniques, Tools, and Applications
Sentic Computing: Techniques, Tools, and Applications
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The passage from a static read-only Web to a dynamic read-write Web gave birth to a huge amount of online social networks with the ultimate goal of making communication easier between people with common interests. Unlike real world social networks, however, online social groups tend to form for extremely varied and multi-faceted reasons. This makes very difficult to group members of the same social network in subsets in a way that certain types of contents are shared with just certain types of friends. Moreover, such a task is usually too tedious to be performed manually and too complex to be performed automatically. In this work, we propose a new approach for automatically clustering social networks, which exploits interaction semantics and sentics, that is, the conceptual and affective information associated with the interactive behavior of online social network members.