Predicting hyperlinks via hypernetwork loop structure

¹ School of Computer and Electronic Information, Nanjing Normal University - Nanjing 210023, China
² Institute of Computer Science, LMU Munich - Munich 80538, Germany
³ The University of Georgia - Athens, GA 30602, USA
⁴ School of Public Health and Management, Chongqing Medical University, - Chongqing 400016, China
⁵ School of Mathematical Sciences, Nanjing Normal University - Nanjing 210023, China

^(a) wwzqbx@hotmail.com (corresponding author)
^(b) tianlx@ujs.edu.cn

Received: 16 March 2021
Accepted: 3 August 2021

Abstract

While links in simple networks describe pairwise interactions between nodes, it is necessary to incorporate hypernetworks for modeling complex systems with arbitrary-sized interactions. In this study, we focus on the hyperlink prediction problem in hypernetworks, for which the current state-of-art methods are latent-feature based. A practical algorithm via topological features, which can provide understandings of the organizational principles of hypernetworks, is still lacking. For simple networks, local clustering or loop reflects the correlations among nodes; therefore, loop-based link prediction algorithms have achieved accurate performance. Extending the idea to hyperlink prediction faces several challenges. For instance, what is an effective way of defining loops for prediction is not clear yet; besides, directly comparing topological statistics of variable-sized hyperlinks could introduce biases in hyperlink cardinality. In this study, we address the issues and propose a loop-based hyperlink prediction approach. First, we discuss and define the loops in hypernetworks; then, we transfer the loop features into a hyperlink prediction algorithm via a simple modified logistic regression. Numerical experiments on multiple real-world datasets demonstrate superior performance compared to the state-of-the-art methods.