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Communication Dans Un Congrès Année : 2021

Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs

Résumé

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.

Dates et versions

hal-03540371 , version 1 (23-01-2022)

Identifiants

Citer

Md Kamrul Islam, Sabeur Aridhi, Malika Smaïl-Tabbone. Appraisal Study of Similarity-Based and Embedding-Based Link Prediction Methods on Graphs. CDKP 2021 - 10th International Conference on Data Mining & Knowledge Management Process, Jul 2021, London, United Kingdom. pp.81-92, ⟨10.5121/csit.2021.111106⟩. ⟨hal-03540371⟩
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