Masoud/류진수’s paper has been accepted in

Title: GELTOR: A Novel Graph Embedding Method based on Listwise Learning to Rank
Author: Masoud Rehyani Hamedani, JinSu Ryu and Sang-Wook Kim
Abstract
Similarity-based embedding methods have introduced a new perspective on graph embedding by conforming the similarity distribution of latent vectors in embedding space to that of nodes in the graph. These methods show significant effectiveness over conventional embedding methods in various machine learning tasks. In this paper, we first point out the three drawbacks of these methods: inaccurate similarity computation, optimization goal conflict, and impairing in/out-degree distributions. Then, motivated by these drawbacks, we propose a novel similarity measure for graphs called AdaSim*, which is conducive to the similarity-based graph embedding. We finally propose GELTOR, an effective graph embedding method that employs AdaSim* as a node similarity measure and the concept of learning-to-rank in the embedding process. Contrary to existing similarity-based methods, GELTOR does not aim to learn the similarity scores distribution. Instead, for any target node 𝑣, GELTOR learns a model that conforms the ranks of 𝑣’s top-𝑡 similar nodes in the embedding space to their original ranks based on AdaSim* scores. The philosophy behind GELTOR is that for any target node 𝑣, exploiting only its top-𝑡 similar nodes is more beneficial to graph embedding than exploiting all the nodes in the graph, since considering dissimilar nodes may adversely affect the learning quality. 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 learning-to-rank is beneficial to graph embedding.