文章基本信息

标题：Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs
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作者：Md Kamrul Islam ; Sabeur Aridhi ; Malika Smail-Tabbone 等
期刊名称：Computer Science & Information Technology
电子版ISSN：2231-5403
出版年度：2021
卷号：11
期号：11
语种：English
出版社：Academy & Industry Research Collaboration Center (AIRCC)
摘要：The task of inferring missing links or predicting future ones in a graph based on its current structure is referred to as link prediction. Link prediction methods that are based on pairwise node similarity are well-established approaches in the literature and show good prediction performance in many real-world graphs though they are heuristic. On the other hand, graph embedding approaches learn low-dimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This appraisal paper studies a selection of methods from both categories on several benchmark (homogeneous) graphs with different properties from various domains. Beyond the intra and inter category comparison of the performances of the methods our aim is also to uncover interesting connections between Graph Neural Network(GNN)-based methods and heuristic ones as a means to alleviate the black-box well-known limitation.
关键词：Link Prediction;Graph Neural Network;Homogeneous Graph & Node Embedding