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  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)  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.
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