ASiNE: Adversarial Signed Network Embedding
- Authors
- Lee, Yeon-Chang; Seo, Nayoun; Han, Kyungsik; Kim, Sang-Wook
- Issue Date
- Jul-2020
- Publisher
- ASSOC COMPUTING MACHINERY
- Keywords
- adversarial learning; balance theory; signed network embedding
- Citation
- PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), pp.609 - 618
- Indexed
- SCOPUS
- Journal Title
- PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
- Start Page
- 609
- End Page
- 618
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145408
- DOI
- 10.1145/3397271.3401079
- Abstract
- Motivated by a success of generative adversarial networks (GAN) in various domains including information retrieval, we propose a novel signed network embedding framework, ASiNE, which represents each node of a given signed network as a low-dimensional vector based on the adversarial learning. To do this, we first design a generator G+ and a discriminator D+ that consider positive edges, as well as a generator G-and a discriminator D-that consider negative edges: (1) G+/G-aim to generate the most indistinguishable fake positive/negative edges, respectsupively; (2) D+/D aim to discriminate between real positive/negative edges and fake positive/negative edges, respectively. Furthermore, under ASiNE, we propose two new strategies for effective signed network embedding: (1) an embedding space sharing strategy for learning both positive and negative edges; (2) a fake edge generation strategy based on the balance theory. Through extensive experiments using five real-life signed networks, we verify the effectiveness of each of the strategies employed in ASiNE. We also show that ASiNE consistently and significantly outperforms all the state-of-the-art signed network embedding methods in all datasets and with all metrics in terms of accuracy of sign prediction.
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