research-article

SigGAN: Adversarial Model for Learning Signed Relationships in Networks

Authors:

Roshni Chakraborty,

Joydeep ChandraAuthors Info & Claims

ACM Transactions on Knowledge Discovery from Data, Volume 17, Issue 1

Article No.: 8, Pages 1 - 20

https://doi.org/10.1145/3532610

Published: 20 February 2023 Publication History

Abstract

Signed link prediction in graphs is an important problem that has applications in diverse domains. It is a binary classification problem that predicts whether an edge between a pair of nodes is positive or negative. Existing approaches for link prediction in unsigned networks cannot be directly applied for signed link prediction due to their inherent differences. Furthermore, signed link prediction must consider the inherent characteristics of signed networks, such as structural balance theory. Recent signed link prediction approaches generate node representations using either generative models or discriminative models. Inspired by the recent success of Generative Adversarial Network (GAN) based models in several applications, we propose a GAN based model for signed networks, SigGAN. It considers the inherent characteristics of signed networks, such as integration of information from negative edges, high imbalance in number of positive and negative edges, and structural balance theory. Comparing the performance with state-of-the-art techniques on five real-world datasets validates the effectiveness of SigGAN.

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Index Terms

SigGAN: Adversarial Model for Learning Signed Relationships in Networks
1. Computer systems organization
  1. Embedded and cyber-physical systems

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Published In

cover image ACM Transactions on Knowledge Discovery from Data

ACM Transactions on Knowledge Discovery from Data Volume 17, Issue 1

January 2023

375 pages

ISSN:1556-4681

EISSN:1556-472X

DOI:10.1145/3572846

Editor:
Charu Aggarwal
IBM T. J. Watson Research, USA

Issue’s Table of Contents

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Publication History

Published: 20 February 2023

Online AM: 20 May 2022

Accepted: 16 April 2022

Revised: 30 March 2022

Received: 29 March 2021

Published in TKDD Volume 17, Issue 1

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Cited By

Anitha RVimal Kumar D(2024)Examining the Impact of User Credibility Score-Based Collaborative Filtering in E-commerce: Leveraging Machine Learning for Vehicle-to-Everything (V2X) Communication2024 International Conference on Trends in Quantum Computing and Emerging Business Technologies10.1109/TQCEBT59414.2024.10545249(1-5)Online publication date: 22-Mar-2024
https://doi.org/10.1109/TQCEBT59414.2024.10545249
Wan S(2024)Learning from Subgraphs and Embeddings for Sign Prediction2024 21st International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)10.1109/ICCWAMTIP64812.2024.10873642(01-05)Online publication date: 14-Dec-2024
https://doi.org/10.1109/ICCWAMTIP64812.2024.10873642
Lv YDuan JLi X(2024)A survey on modeling for behaviors of complex intelligent systems based on generative adversarial networksComputer Science Review10.1016/j.cosrev.2024.10063552(100635)Online publication date: May-2024
https://doi.org/10.1016/j.cosrev.2024.100635
Sun HTian PXiong YZhang YXiang YJia XWang H(2024)DynamiSE: dynamic signed network embedding for link predictionMachine Language10.1007/s10994-023-06473-z113:7(4037-4053)Online publication date: 23-Jan-2024
https://dl.acm.org/doi/10.1007/s10994-023-06473-z

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