research-article

Link Prediction in Heterogeneous Social Networks

Authors:

Sumit Negi,

Santanu ChaudhuryAuthors Info & Claims

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

Pages 609 - 617

https://doi.org/10.1145/2983323.2983722

Published: 24 October 2016 Publication History

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Abstract

A heterogeneous social network is characterized by multiple link types which makes the task of link prediction in such networks more involved. In the last few years collective link prediction methods have been proposed for the problem of link prediction in heterogeneous networks. These methods capture the correlation between different types of links and utilize this information in the link prediction task. In this paper we pose the problem of link prediction in heterogeneous networks as a multi-task, metric learning (MTML) problem. For each link-type (relation) we learn a corresponding distance measure, which utilizes both network and node features. These link-type specific distance measures are learnt in a coupled fashion by employing the Multi-Task Structure Preserving Metric Learning (MT-SPML) setup. We further extend the MT-SPML method to account for task correlations, robustness to non-informative features and non-stationary degree distribution across networks. Experiments on the Flickr and DBLP network demonstrates the effectiveness of our proposed approach vis-à-vis competitive baselines.

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Cited By

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Balvir SRaghuwanshi MShobhane P(2024)An Overview of Similarity-Based Methods in Predicting Social Network Links: A Comparative AnalysisIEEE Access10.1109/ACCESS.2024.345050612(120913-120934)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3450506
Zhang CLi KWang SZhou BWang LSun F(2023)Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link PredictionMathematics10.3390/math1103057811:3(578)Online publication date: 21-Jan-2023
https://doi.org/10.3390/math11030578
Wu JHu YHuang ZLi JLi XSha Y(2023)Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social NetworksApplied Sciences10.3390/app13221243713:22(12437)Online publication date: 17-Nov-2023
https://doi.org/10.3390/app132212437
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Index Terms

Link Prediction in Heterogeneous Social Networks
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. World Wide Web
    1. Web applications
      1. Social networks
2. Theory of computation
  1. Theory and algorithms for application domains

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Published In

CIKM '16: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management

October 2016

2566 pages

ISBN:9781450340731

DOI:10.1145/2983323

General Chairs:
Snehasis Mukhopadhyay
Indiana University Purdue University Indianapolis, USA
,
ChengXiang Zhai
University of Illinois at Urbana-Champaign, USA
,
Program Chairs:
Elisa Bertino
Purdue University
,
Fabio Crestani
University of Lugano
,
Javed Mostafa
University of North Carolina
,
Jie Tang
Tsinghua University
,
Luo Si
Alibaba Group Inc & Purdue University
,
Xiaofang Zhou
University of Queensland
,
Yi Chang
Yahoo Research
,
Yunyao Li
IBM Research - Almaden
,
Parikshit Sondhi
WalmartLabs

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Published: 24 October 2016

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CIKM'16: ACM Conference on Information and Knowledge Management

October 24 - 28, 2016

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CIKM '16 Paper Acceptance Rate 160 of 701 submissions, 23%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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Cited By

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Balvir SRaghuwanshi MShobhane P(2024)An Overview of Similarity-Based Methods in Predicting Social Network Links: A Comparative AnalysisIEEE Access10.1109/ACCESS.2024.345050612(120913-120934)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3450506
Zhang CLi KWang SZhou BWang LSun F(2023)Learning Heterogeneous Graph Embedding with Metapath-Based Aggregation for Link PredictionMathematics10.3390/math1103057811:3(578)Online publication date: 21-Jan-2023
https://doi.org/10.3390/math11030578
Wu JHu YHuang ZLi JLi XSha Y(2023)Enhancing Predictive Expert Method for Link Prediction in Heterogeneous Information Social NetworksApplied Sciences10.3390/app13221243713:22(12437)Online publication date: 17-Nov-2023
https://doi.org/10.3390/app132212437
Wang HLiu RShi CChen JFang LLiu SGong Z(2023)Resisting the Edge-Type Disturbance for Link Prediction in Heterogeneous NetworksACM Transactions on Knowledge Discovery from Data10.1145/361409918:2(1-24)Online publication date: 7-Aug-2023
https://dl.acm.org/doi/10.1145/3614099
Fotsing CShen CHuang LChen YLee WYang D(2023)On Efficient Processing of Queries for Live Multi-Streaming Soiree OrganizationIEEE Transactions on Services Computing10.1109/TSC.2023.324832116:4(2812-2826)Online publication date: 1-Jul-2023
https://doi.org/10.1109/TSC.2023.3248321
Wang HCui ZLiu RFang LSha Y(2023)A Multi-Type Transferable Method for Missing Link Prediction in Heterogeneous Social NetworksIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2022.323348135:11(10981-10991)Online publication date: 1-Nov-2023
https://doi.org/10.1109/TKDE.2022.3233481
Phan CNguyen QNguyen K(2023)Link Prediction for Wikipedia Articles as a Natural Language Inference Task2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA60987.2023.10302560(1-4)Online publication date: 9-Oct-2023
https://doi.org/10.1109/DSAA60987.2023.10302560
Xu YFeng ZXing MWu HChen SXue XDustdar S(2023)Metapath-guided multi-headed attention networks for trust prediction in heterogeneous social networksKnowledge-Based Systems10.1016/j.knosys.2023.111119282(111119)Online publication date: Dec-2023
https://doi.org/10.1016/j.knosys.2023.111119
Zhao YSun YHuang YLi LDong H(2023)Link prediction in heterogeneous networks based on metapath projection and aggregationExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.120325227:COnline publication date: 11-Jul-2023
https://dl.acm.org/doi/10.1016/j.eswa.2023.120325
Khaksar Manshad MMeybodi MSalajegheh A(2022)A new multi-wave continuous action-set cellular learning automata for link prediction problem in weighted multi-layer social networksThe Journal of Supercomputing10.1007/s11227-022-04615-z78:17(18636-18665)Online publication date: 9-Jun-2022
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