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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.<\/jats:p>","DOI":"10.1145\/3532610","type":"journal-article","created":{"date-parts":[[2022,5,20]],"date-time":"2022-05-20T12:25:42Z","timestamp":1653049542000},"page":"1-20","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["SigGAN: Adversarial Model for Learning Signed Relationships in Networks"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4476-403X","authenticated-orcid":false,"given":"Roshni","family":"Chakraborty","sequence":"first","affiliation":[{"name":"Indian Institute of Technology Patna, Patna, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1321-2497","authenticated-orcid":false,"given":"Ritwika","family":"Das","sequence":"additional","affiliation":[{"name":"National Institute of Technology Durgapur, Durgapur, India"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5994-9024","authenticated-orcid":false,"given":"Joydeep","family":"Chandra","sequence":"additional","affiliation":[{"name":"Indian Institute of Technology Patna, Patna, India"}]}],"member":"320","published-online":{"date-parts":[[2023,2,20]]},"reference":[{"key":"e_1_3_2_2_2","first-page":"2591","volume-title":"Proceedings of the 23rd International Joint Conference on Artificial Intelligence","author":"Agrawal Priyanka","year":"2013","unstructured":"Priyanka Agrawal, Vikas K. 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