GELTOR: A Graph Embedding Method based on Listwise Learning to Rank
DC Field | Value | Language |
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dc.contributor.author | Reyhani Hamedani, Masoud | - |
dc.contributor.author | Ryu, Jin-Su | - |
dc.contributor.author | Kim, Sang-Wook | - |
dc.date.accessioned | 2023-06-01T07:00:22Z | - |
dc.date.available | 2023-06-01T07:00:22Z | - |
dc.date.issued | 2023-04 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185838 | - |
dc.description.abstract | Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in the embedding space to that of nodes in the graph; they show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of existing similarity-based embedding methods: inaccurate similarity computation, conflicting optimization goal, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose AdaSim∗, a novel similarity measure for graphs that is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective embedding method that employs AdaSim∗as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing methods, GELTOR does not learn the similarity scores distribution; instead, for any target node, GELTOR conforms the ranks of its top-t similar nodes in the embedding space to their original ranks based on AdaSim∗scores. We conduct extensive experiments with six real-world datasets to evaluate the effectiveness of GELTOR in graph reconstruction, link prediction, and node classification tasks. Our experimental results show that (1) AdaSim∗outperforms AdaSim, RWR, and MCT in computing nodes similarity in graphs, (2) our GETLOR outperforms existing state-of-the-arts and conventional embedding methods in most cases of the above machine learning tasks, thereby implying that learning-to-rank is beneficial to graph embedding. | - |
dc.format.extent | 11 | - |
dc.language | 영어 | - |
dc.language.iso | ENG | - |
dc.publisher | Association for Computing Machinery, Inc | - |
dc.title | GELTOR: A Graph Embedding Method based on Listwise Learning to Rank | - |
dc.type | Article | - |
dc.publisher.location | 미국 | - |
dc.identifier.doi | 10.1145/3543507.3583193 | - |
dc.identifier.scopusid | 2-s2.0-85159267684 | - |
dc.identifier.bibliographicCitation | ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, pp 6 - 16 | - |
dc.citation.title | ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023 | - |
dc.citation.startPage | 6 | - |
dc.citation.endPage | 16 | - |
dc.type.docType | Conference paper | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordPlus | Arts computing | - |
dc.subject.keywordPlus | Classification (of information) | - |
dc.subject.keywordPlus | Graph algorithms | - |
dc.subject.keywordPlus | Graph neural networks | - |
dc.subject.keywordPlus | Graph structures | - |
dc.subject.keywordPlus | Machine learning | - |
dc.subject.keywordPlus | Vector spaces | - |
dc.subject.keywordPlus | Graph embeddings | - |
dc.subject.keywordPlus | Embedding method | - |
dc.subject.keywordPlus | Graph embeddings | - |
dc.subject.keywordPlus | Learning tasks | - |
dc.subject.keywordPlus | Link-based | - |
dc.subject.keywordPlus | Link-based similarity | - |
dc.subject.keywordPlus | Machine-learning | - |
dc.subject.keywordPlus | Node similarities | - |
dc.subject.keywordPlus | Similarity distribution | - |
dc.subject.keywordPlus | Similarity measure | - |
dc.subject.keywordAuthor | graph embedding | - |
dc.subject.keywordAuthor | learning-to-rank | - |
dc.subject.keywordAuthor | link-based similarity | - |
dc.identifier.url | https://dl.acm.org/doi/10.1145/3543507.3583193 | - |
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