Naty Leiser
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Suggesting (More) Friends Using the Implicit Social Graph
Maayan Roth
Tzvika Barenholz
Assaf Ben-David
Guy Flysher
Ilan Horn
Ari Leichtberg
Ron Merom
International Conference on Machine Learning (ICML) (2011)
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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 features.
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Suggesting Friends Using the Implicit Social Graph
Maayan Roth
Assaf Ben-David
Guy Flysher
Ilan Horn
Ari Leichtberg
Ron Merom
Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (2010)
Preview abstract
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.
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Pregel: a system for large-scale graph processing
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Grzegorz Malewicz
Matthew H. Austern
James C. Dehnert
Ilan Horn
Grzegorz Czajkowski
Proceedings of the 2010 international conference on Management of data, ACM, New York, NY, USA, pp. 135-146
Pregel: A System for Large-Scale Graph Processing
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Grzegorz Malewicz
Matthew H. Austern
James C. Dehnert
Ilan Horn
Grzegorz Czajkowski
28th ACM Symposium on Principles of Distributed Computing (2009), pp. 6-6
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