Cluster Ranking with an Application to Mining Mailbox Networks
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Predicting tie strength with social media
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Behavioral profiles for advanced email features
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Cluestr: mobile social networking for enhanced group communication
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Inferring relevant social networks from interpersonal communication
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Ranking users for intelligent message addressing
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Visual analysis of implicit social networks for suspicious behavior detection
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Regroup: interactive machine learning for on-demand group creation in social networks
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Automatic foldering of email messages: a combination approach
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Clustering social networks using interaction semantics and sentics
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Measuring tie strength in implicit social networks
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Finding email correspondents in online social networks
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Integrating multiple contexts in real-time collaboration applications
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Transforming graph data for statistical relational learning
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Personalized recommendation based on implicit social network of researchers
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Social influence based clustering of heterogeneous information networks
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Unified entity search in social media community
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Identifying unreliable sources of skill and competency information
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Evolving friend lists in social networks
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Indirect weighted association rules mining for academic network collaboration recommendations
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
Machine Learning
Discovering implicit communities in Web forums through ontologies
Web Intelligence and Agent Systems
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Although users of online communication tools rarely categorize their contacts into groups such as "family", "co-workers", or "jogging buddies", they nonetheless implicitly cluster contacts, by virtue of their interactions with them, forming implicit groups. In this paper, we describe the implicit social graph which is formed by users' interactions with contacts and groups of contacts, and which is distinct from explicit social graphs in which users explicitly add other individuals as their "friends". We introduce an interaction-based metric for estimating a user's affinity to his contacts and groups. We then describe a novel friend suggestion algorithm that uses a user's implicit social graph to generate a friend group, given a small seed set of contacts which the user has already labeled as friends. We show experimental results that demonstrate the importance of both implicit group relationships and interaction-based affinity ranking in suggesting friends. Finally, we discuss two applications of the Friend Suggest algorithm that have been released as Gmail Labs features.