유송경/이다은/고윤용’s paper has been accepted in

Title: HyGEN: Regularizing Negative Hyperedge Generation for Accurate Hyperedge Prediction
Author: Song Kyung Yu, Da Eun Lee, Yunyong Ko, and Sang-Wook Kim
Abstract
Hyperedge prediction is a fundamental task to predict future high-order relations based on the observed network structure. Existing hyperedge prediction methods, however, suffer from the data sparsity problem. To alleviate this problem, negative sampling methods can be used, which leverage non-existing hyperedges as contrastive information for model training. However, the following important challenges have been rarely studied: (C1) lack of guidance for generating negatives and (C2) possibility of false negatives. To address them, we propose a novel hyperedge prediction method, HyGEN, that employs (1) a positive-guided generator using positive hyperedges as a guidance to generate more realistic negative hyperedges and (2) a regularization term to prevent the generated hyperedges from being false negatives. Extensive experiments on six real-world hypergraphs reveal that HyGEN consistently outperforms four state-of-the-art hyperedge prediction methods.