ASiNE: Adversarial Signed Network Embedding
DC Field | Value | Language |
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dc.contributor.author | Lee, Yeon-Chang | - |
dc.contributor.author | Seo, Nayoun | - |
dc.contributor.author | Han, Kyungsik | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2022-07-07T22:14:07Z | - |
dc.date.available | 2022-07-07T22:14:07Z | - |
dc.date.created | 2021-05-13 | - |
dc.date.issued | 2020-07 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/145408 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | ASSOC COMPUTING MACHINERY | - |
dc.title | ASiNE: Adversarial Signed Network Embedding | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kim, Sang-Wook | - |
dc.identifier.doi | 10.1145/3397271.3401079 | - |
dc.identifier.scopusid | 2-s2.0-85090161314 | - |
dc.identifier.wosid | 000722377700069 | - |
dc.identifier.bibliographicCitation | PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20), pp.609 - 618 | - |
dc.relation.isPartOf | PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) | - |
dc.citation.title | PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20) | - |
dc.citation.startPage | 609 | - |
dc.citation.endPage | 618 | - |
dc.type.rims | ART | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
dc.subject.keywordPlus | Information retrieval | - |
dc.subject.keywordPlus | Silicon | - |
dc.subject.keywordPlus | Adversarial learning | - |
dc.subject.keywordPlus | Adversarial networks | - |
dc.subject.keywordPlus | First designs | - |
dc.subject.keywordPlus | Low dimensional | - |
dc.subject.keywordPlus | Positive/negative | - |
dc.subject.keywordPlus | Signed networks | - |
dc.subject.keywordPlus | Space-sharing | - |
dc.subject.keywordPlus | State of the art | - |
dc.subject.keywordPlus | Embeddings | - |
dc.subject.keywordAuthor | adversarial learning | - |
dc.subject.keywordAuthor | balance theory | - |
dc.subject.keywordAuthor | signed network embedding | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3397271.3401079 | - |
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