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How user behavior is related to social affinity

Published: 08 February 2012 Publication History

Abstract

Previous research has suggested that people who are in the same social circle exhibit similar behaviors and tastes. The rise of social networks gives us insights into the social circles of web users, and recommendation services (including search engines, advertisement engines, and collaborative filtering engines) provide a motivation to adapt recommendations to the interests of the audience. An important primitive for supporting these applications is the ability to quantify how connected two users are in a social network. The shortest-path distance between a pair of users is an obvious candidate measure. This paper introduces a new measure of "affinity" in social networks that takes into account not only the distance between two users, but also the number of edge-disjoint paths between them, i.e. the "robustness" of their connection. Our measure is based on a sketch-based approach, and affinity queries can be answered extremely efficiently (at the expense of a one-time offline sketch computation). We compare this affinity measure against the "approximate shortest-path distance", a sketch-based distance measure with similar efficiency characteristics. Our empirical study is based on a Hotmail email exchange graph combined with demographic information and Bing query history, and a Twitter mention-graph together with the text of the underlying tweets. We found that users who are close to each other - either in terms of distance or affinity - have a higher similarity in terms of demographics, queries, and tweets.

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cover image ACM Conferences
WSDM '12: Proceedings of the fifth ACM international conference on Web search and data mining
February 2012
792 pages
ISBN:9781450307475
DOI:10.1145/2124295
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: 08 February 2012

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Author Tags

  1. affinity
  2. distance
  3. influence
  4. sketching
  5. social networks

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Cited By

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  • (2022)A Comprehensive Survey on Affinity Analysis, Bibliomining, and Technology Mining: Past, Present, and Future ResearchApplied Sciences10.3390/app1210522712:10(5227)Online publication date: 21-May-2022
  • (2021)Private Hierarchical Clustering in Federated NetworksProceedings of the 2021 ACM SIGSAC Conference on Computer and Communications Security10.1145/3460120.3484822(2342-2360)Online publication date: 12-Nov-2021
  • (2020)A new approach for affinity relationship discovery in online forumsSocial Network Analysis and Mining10.1007/s13278-020-00644-910:1Online publication date: 4-Jun-2020
  • (2019)HAR-searchProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3342888(176-183)Online publication date: 27-Aug-2019
  • (2019)On-Device Algorithms for Public-Private Data with Absolute PrivacyThe World Wide Web Conference10.1145/3308558.3313677(405-416)Online publication date: 13-May-2019
  • (2019)Community DetectionBroad Learning Through Fusions10.1007/978-3-030-12528-8_8(275-314)Online publication date: 9-Jun-2019
  • (2018)The Influence of Community Interactions on User Affinity in Social Networks: A Facebook Case Study2018 National Information Technology Conference (NITC)10.1109/NITC.2018.8550063(1-6)Online publication date: Oct-2018
  • (2017)Patterns of Twitter Behavior Among Networks of Cannabis Dispensaries in CaliforniaJournal of Medical Internet Research10.2196/jmir.713719:7(e236)Online publication date: 4-Jul-2017
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