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Unsupervised Feature Selection in Signed Social Networks

Authors:

Huan LiuAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 777 - 786

https://doi.org/10.1145/3097983.3098106

Published: 04 August 2017 Publication History

Abstract

The rapid growth of social media services brings a large amount of high-dimensional social media data at an unprecedented rate. Feature selection is powerful to prepare high-dimensional data by finding a subset of relevant features. A vast majority of existing feature selection algorithms for social media data exclusively focus on positive interactions among linked instances such as friendships and user following relations. However, in many real-world social networks, instances may also be negatively interconnected. Recent work shows that negative links have an added value over positive links in advancing many learning tasks. In this paper, we study a novel problem of unsupervised feature selection in signed social networks and propose a novel framework SignedFS. In particular, we provide a principled way to model positive and negative links for user latent representation learning. Then we embed the user latent representations into feature selection when label information is not available. Also, we revisit the principle of homophily and balance theory in signed social networks and incorporate the signed graph regularization into the feature selection framework to capture the first-order and the second-order proximity among users in signed social networks. Experiments on two real-world signed social networks demonstrate the effectiveness of our proposed framework. Further experiments are conducted to understand the impacts of different components of SignedFS.

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Cited By

Chakraborty RDas RChandra J(2023)SigGAN: Adversarial Model for Learning Signed Relationships in NetworksACM Transactions on Knowledge Discovery from Data10.1145/353261017:1(1-20)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532610
Jung JYoo JKang U(2022)Signed random walk diffusion for effective representation learning in signed graphsPLOS ONE10.1371/journal.pone.026500117:3(e0265001)Online publication date: 17-Mar-2022
https://doi.org/10.1371/journal.pone.0265001
Liang BKang GLiu JCao BXiang J(2022)Attentional Neural Factorization Machine for Web Services Classification via Exploring Content and Structural Semantics2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892320(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892320
Show More Cited By

Index Terms

Unsupervised Feature Selection in Signed Social Networks
1. Computing methodologies
  1. Machine learning
    1. Machine learning algorithms
      1. Feature selection

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cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

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Published: 04 August 2017

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August 13 - 17, 2017

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

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Cited By

Chakraborty RDas RChandra J(2023)SigGAN: Adversarial Model for Learning Signed Relationships in NetworksACM Transactions on Knowledge Discovery from Data10.1145/353261017:1(1-20)Online publication date: 20-Feb-2023
https://dl.acm.org/doi/10.1145/3532610
Jung JYoo JKang U(2022)Signed random walk diffusion for effective representation learning in signed graphsPLOS ONE10.1371/journal.pone.026500117:3(e0265001)Online publication date: 17-Mar-2022
https://doi.org/10.1371/journal.pone.0265001
Liang BKang GLiu JCao BXiang J(2022)Attentional Neural Factorization Machine for Web Services Classification via Exploring Content and Structural Semantics2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9892320(1-8)Online publication date: 18-Jul-2022
https://doi.org/10.1109/IJCNN55064.2022.9892320
Zhang LMoskwa NLarsen MBogdanov P(2022)Unsupervised Instance and Subnetwork Selection for Network Data2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)10.1109/DSAA54385.2022.10032410(1-10)Online publication date: 13-Oct-2022
https://doi.org/10.1109/DSAA54385.2022.10032410
Moudjari LAkli-Astouati K(2022)Construction and Exploitation of an Algerian Corpus for Opinion and Emotion AnalysisAdvances in Knowledge Discovery and Management10.1007/978-3-030-90287-2_1(3-23)Online publication date: 15-Mar-2022
https://doi.org/10.1007/978-3-030-90287-2_1
Jing YWang HShao KHuo X(2021)Relation Representation Learning via Signed Graph Mutual Information Maximization for Trust PredictionSymmetry10.3390/sym1301011513:1(115)Online publication date: 11-Jan-2021
https://doi.org/10.3390/sym13010115
Xiao YLiu JKang GCao B(2021)LDNM: A General Web Service Classification Framework via Deep Fusion of Structured and Unstructured FeaturesIEEE Transactions on Network and Service Management10.1109/TNSM.2021.308473918:3(3858-3872)Online publication date: Sep-2021
https://doi.org/10.1109/TNSM.2021.3084739
Ma YTang J(2021)Deep Learning on Graphs10.1017/9781108924184Online publication date: 2-Sep-2021
https://doi.org/10.1017/9781108924184
Sun XYu YLiang YDong JPlant CBöhm C(2021)Fusing attributed and topological global-relations for network embeddingInformation Sciences10.1016/j.ins.2021.01.012558(76-90)Online publication date: May-2021
https://doi.org/10.1016/j.ins.2021.01.012
Schwaiger JHammerl TFlorian JLeist S(2021)UR: SMART–A tool for analyzing social media contentInformation Systems and e-Business Management10.1007/s10257-021-00541-419:4(1275-1320)Online publication date: 16-Sep-2021
https://dl.acm.org/doi/10.1007/s10257-021-00541-4
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