Hypergraph Representation Learning with Adaptive Broadcasting and Receiving

Tianyi Ma, Yiyue Qian, Zheyuan Zhang, Zehong Wang, Shinan Zhang, Chuxu Zhang, Yanfang Ye

Abstract: Hypergraphs, in contrast to general graphs, utilize hyperedges to connect multiple nodes, thereby inherently facilitating the representation of higher-order relational structures. To leverage the benefits of hypergraphs, several Hypergraph Neural Networks (HyGNNs) have been proposed to model hypergraph structures. Although existing HyGNNs excel at capturing complex relationships in homophilic hypergraphs, they still face challenges in modeling heterophilic hypergraphs, as most existing HyGNNs are designed based on the homophily principle. Recent studies have attempted to leverage attention mechanisms that are less reliant on the homophily principle. However, these attention mechanisms remain ineffective for nodes in heterophilic hypergraphs. To tackle the aforementioned challenges, we propose a novel Broadcast HyperGraph Neural Network (BHyGNN) to adaptively broadcast node information to learn more effective node representations in heterophilic hypergraphs. Specifically, we devise a novel Variational Broadcast Autoencoder Network to sample the broadcast and receive actions to propagate information between nodes and hyperedges. Moreover, we design an incorporation transformer mechanism to perform the estimated broadcast or receive actions to learn the hyperedge or node representations, incorporating the information from both sides. Extensive experiments over five benchmark heterophilic hypergraph datasets and six homophilic hypergraph datasets demonstrate the effectiveness of BHyGNN over all baseline methods. Our source code and datasets are available at https://github.com/Tianyi-Billy-Ma/BHyGNN.

External IDs:dblp:conf/icdm/MaQZWZZY25