GELTOR: A Graph Embedding Method based on Listwise Learning to Rank
- Authors
- Reyhani Hamedani, Masoud; Ryu, Jin-Su; Kim, Sang-Wook
- Issue Date
- Apr-2023
- Publisher
- Association for Computing Machinery, Inc
- Keywords
- graph embedding; learning-to-rank; link-based similarity
- Citation
- ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023, pp 6 - 16
- Pages
- 11
- Indexed
- SCOPUS
- Journal Title
- ACM Web Conference 2023 - Proceedings of the World Wide Web Conference, WWW 2023
- Start Page
- 6
- End Page
- 16
- URI
- https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/185838
- DOI
- 10.1145/3543507.3583193
- 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.
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