An Experimental Evaluation of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

Md Kamrul Islam, Sabeur Aridhi and Malika Smail-Tabbone, Universite de Lorraine, France

ABSTRACT

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 realworld graphs though they are heuristic. On the other hand, graph embedding approaches learn lowdimensional representation of nodes in graph and are capable of capturing inherent graph features, and thus support the subsequent link prediction task in graph. This 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.

KEYWORDS

Link Prediction, Graph Neural Network, Homogeneous Graph & Node Embedding

Original Source URL: https://aircconline.com/ijdkp/V11N5/11521ijdkp01.pdf

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