Collaborative Filtering Approach to Link Prediction

Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for som...

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Bibliographic Details
Published inarXiv.org
Main Authors Yan-Li, Lee, Zhou, Tao
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 14.03.2021
Subjects
Algorithms
Collaboration
Computer Science - Information Retrieval
Computer Science - Social and Information Networks
Filtration
Network analysis
Recommender systems
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Summary:Link prediction is a fundamental challenge in network science. Among various methods, local similarity indices are widely used for their high cost-performance. However, the performance is less robust: for some networks local indices are highly competitive to state-of-the-art algorithms while for some other networks they are very poor. Inspired by techniques developed for recommender systems, we propose an enhancement framework for local indices based on collaborative filtering (CF). Considering the delicate but important difference between personalized recommendation and link prediction, we further propose an improved framework named as self-included collaborative filtering (SCF). The SCF framework significantly improved the accuracy and robustness of well-known local indices. The combination of SCF framework and a simple local index can produce an index with competitive performance and much lower complexity compared with elaborately-designed state-of-the-art algorithms.
ISSN:2331-8422
DOI:10.48550/arxiv.2103.09907