ASiNE: Adversarial Signed Network Embedding
- Title
- ASiNE: Adversarial Signed Network Embedding
- Author
- 김상욱
- Keywords
- signed network embedding; adversarial learning; balance theory
- Issue Date
- 2020-07
- Publisher
- ACM SIGIR 2020
- Citation
- Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, page. 609-618
- 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, respectively; (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.
- URI
- https://dl.acm.org/doi/10.1145/3397271.3401079https://repository.hanyang.ac.kr/handle/20.500.11754/169261
- ISBN
- 978-1-4503-8016-4
- DOI
- 10.1145/3397271.3401079
- Appears in Collections:
- COLLEGE OF ENGINEERING[S](공과대학) > COMPUTER SCIENCE(컴퓨터소프트웨어학부) > Articles
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