Predicting hyperlinks via hypernetwork loop structure

TL;DR

This paper introduces a novel loop-based hyperlink prediction method for hypernetworks, leveraging topological features to improve prediction accuracy over existing latent-feature-based approaches.

Contribution

It defines loops in hypernetworks and develops a simple logistic regression algorithm using loop features for hyperlink prediction.

Findings

Outperforms state-of-the-art methods in real-world datasets

Provides topological insights into hypernetwork organization

Addresses challenges in defining loops for variable-sized hyperlinks

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.