Student Jing Huang's paper "UniGNN: a Unified Framework for Graph and Hypergraph Neural Networks" was accepted by the International Joint Conference on Artificial Intelligence (IJCAI 2021).

      This paper proposes UniGNN, a unified framework for interpreting the message passing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs. In this framework, meticulously-designed architectures aiming to deepen GNNs can also be incorporated into hypergraphs with the least effort. Extensive experiments have been conducted to demonstrate the effectiveness of UniGNN on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin. It further proves that the proposed message-passing based UniGNN models are at most as powerful as the 1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraphs. 

      The code is available at https://github.com/OneForward/UniGNN