Hyperedge Prediction in Hypergraphs

Hyperedge Prediction in Hypergraphs

◦ Hypergraphs are higher-order graphs where a relation (a hyperedge or hyperlink) exists between a set of entities as opposed to a Graph where a relation (an edge) can exist only between a pair of entities. ◦ Hyperedge prediction is the task of prediction missing hyperedges or future hyperedges given the knowledge of existing hyperedges. Key Contributions ◦ We are first to explore the impact of negative sampling techniques [1] used for the hyperedge prediction task. In addition, we also propose novel negative sampling techniques which improve upon techniques widely used in literature. ◦ We further propose a clique-closure hypothesis (CCH) [2] for formation of hyperedges in a hypergraph. Our proposed C3MM algorithm, which embeds CCH hypothesis, out performs existing methods on the hyperedge prediction task. ◦ We also establish a sub-higher order (SHO) paradigm for hyperedge evolution and propose a novel neural network architecture, named SHONeN, which shows that SHO paradigm is better at predicting future hyperedges than existing 2O and HO paradigms.

Publications

1 Title: Negative sampling for hyperlink prediction in networks. Authors: Prasanna Patil, Govind Sharma, and M Narasimha Murty. Publication Venue: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2020 2 Title: C3MM: Clique-closure based hyperlink prediction. Authors: Govind Sharma, Prasanna Patil, and M Narasimha Murty. Publication Venue: International Joint Conference on Artificial Intelligence, 2020