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Suggesting friends using the implicit social graph

Published: 25 July 2010 Publication History

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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cover image ACM Conferences
KDD '10: Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
July 2010
1240 pages
ISBN:9781450300551
DOI:10.1145/1835804
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 25 July 2010

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Author Tags

  1. contact group clustering
  2. implicit social graph
  3. tie strength

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  • (2023)Sentiment Analysis on Social Media Data: A SurveyInnovations in Computer Science and Engineering10.1007/978-981-19-7455-7_59(735-745)Online publication date: 4-May-2023
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