Hyperedge Prediction using Tensor Eigenvalue Decomposition

TL;DR

This paper introduces a novel tensor eigenvalue decomposition approach for hyperedge prediction in hypergraphs, leveraging the Fiedler eigenvector to evaluate and identify the most probable hyperedges to form.

Contribution

It proposes a new hyperedge prediction algorithm based on tensor eigenvalue decomposition of hypergraph Laplacian, extending link prediction to complex hypergraph structures.

Findings

Effective hyperedge prediction demonstrated on real datasets

Tensor eigenvalue decomposition provides a novel insight into hypergraph structure

Algorithm outperforms traditional dyadic link prediction methods

Abstract

Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require the user to model super-dyadic associations among nodes. Such interactions can be modeled using a hypergraph, which is a generalization of a graph where a hyperedge can connect more than two nodes. In this work, we consider the problem of hyperedge prediction in a $k-$uniform hypergraph. We utilize the tensor-based representation of hypergraphs and propose a novel interpretation of the tensor eigenvectors. This is further used to propose a hyperedge prediction algorithm. The proposed algorithm utilizes the \textit{Fiedler} eigenvector computed using tensor eigenvalue decomposition of hypergraph Laplacian. The \textit{Fiedler} eigenvector is used to evaluate the construction cost of new hyperedges, which is further utilized to determine the most probable hyperedges to be constructed. The functioning and efficacy of the proposed method are illustrated using some example hypergraphs and a few real datasets. The code for the proposed method is available on https://github.com/d-maurya/hypred_ tensorEVD