research-article

COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency

Authors:

Philip S. YuAuthors Info & Claims

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 1485 - 1494

https://doi.org/10.1145/2783258.2783268

Published: 10 August 2015 Publication History

Abstract

More often than not, people are active in more than one social network. Identifying users from multiple heterogeneous social networks and integrating the different networks is a fundamental issue in many applications. The existing methods tackle this problem by estimating pairwise similarity between users in two networks. However, those methods suffer from potential inconsistency of matchings between multiple networks.

In this paper, we propose COSNET (COnnecting heterogeneous Social NETworks with local and global consistency), a novel energy-based model, to address this problem by considering both local and global consistency among multiple networks. An efficient subgradient algorithm is developed to train the model by converting the original energy-based objective function into its dual form.

We evaluate the proposed model on two different genres of data collections: SNS and Academia, each consisting of multiple heterogeneous social networks. Our experimental results validate the effectiveness and efficiency of the proposed model. On both data collections, the proposed COSNET method significantly outperforms several alternative methods by up to 10-30% (p << 0:001, t-test) in terms of F1-score. We also demonstrate that applying the integration results produced by our method can improve the accuracy of expert finding, an important task in social networks.

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https://doi.org/10.1111/hequ.70009
Jiang SQiu YMo XTang RWang W(2025)An Effective Node Injection Approach for Attacking Social Network AlignmentIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351584220(589-604)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3515842
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Index Terms

COSNET: Connecting Heterogeneous Social Networks with Local and Global Consistency
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '15: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2015

2378 pages

ISBN:9781450336642

DOI:10.1145/2783258

General Chairs:
Longbing Cao
University of Technology, Sydney
,
Chengqi Zhang
University of Technology, Sydney
,
Program Chairs:
Thorsten Joachims
Cornell University
,
Geoff Webb
Monash University
,
Dragos D. Margineantu
Boeing Research
,
Graham Williams
Australian Taxation Office

Copyright © 2015 ACM.

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Published: 10 August 2015

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National Basic Research Program of China
Natural Science Foundation of China
National High-tech R\&D Program

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KDD '15

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KDD '15: The 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 10 - 13, 2015

NSW, Sydney, Australia

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KDD '15 Paper Acceptance Rate 160 of 819 submissions, 20%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.1111/hequ.70009
Jiang SQiu YMo XTang RWang W(2025)An Effective Node Injection Approach for Attacking Social Network AlignmentIEEE Transactions on Information Forensics and Security10.1109/TIFS.2024.351584220(589-604)Online publication date: 2025
https://doi.org/10.1109/TIFS.2024.3515842
Tang RYong ZJiang SChen XLiu YZhang YSun GWang W(2025)Network alignmentPhysics Reports10.1016/j.physrep.2024.11.0061107(1-45)Online publication date: Mar-2025
https://doi.org/10.1016/j.physrep.2024.11.006
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Do MShin KBaeza-Yates RBonchi F(2024)Unsupervised Alignment of Hypergraphs with Different ScalesProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671955(609-620)Online publication date: 25-Aug-2024
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