Higher-order link prediction via local information

Bo Liu; Rongmei Yang; Linyuan Lü

doi:10.1063/5.0135640

Higher-order link prediction via local information

Chaos. 2023 Aug 1;33(8):083108. doi: 10.1063/5.0135640.

Authors

Bo Liu^{1

2}, Rongmei Yang¹, Linyuan Lü^{1

2

3}

Affiliations

¹ Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.
² Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, People's Republic of China.
³ School of Cyber Science and Technology, University of Science and Technology of China, Hefei 230026, People's Republic of China.

PMID: 37535020
DOI: 10.1063/5.0135640

Abstract

Link prediction has been widely studied as an important research direction. Higher-order link prediction has gained, in particular, significant attention since higher-order networks provide a more accurate description of real-world complex systems. However, higher-order networks contain more complex information than traditional pairwise networks, making the prediction of higher-order links a formidable challenging task. Recently, researchers have discovered that local features have advantages over long-range features in higher-order link prediction. Therefore, it is necessary to develop more efficient and concise higher-order link prediction algorithms based on local features. In this paper, we proposed two similarity metrics via local information, simplicial decomposition weight and closed ratio weight, to predict possible future higher-order interactions (simplices) in simplicial networks. These two algorithms capture local higher-order information at two aspects: simplex decomposition and cliques' state (closed or open). We tested their performance in eight empirical simplicial networks, and the results show that our proposed metrics outperform other benchmarks in predicting third-order and fourth-order interactions (simplices) in most cases. In addition, we explore the robustness of the proposed algorithms, and the results suggest that the performance of these novel algorithms is advanced under different sizes of training sets.