UniGNN is a unified framework for interpreting the messagepassing process in graph and hypergraph neural networks, which can generalize general GNN models into hypergraphs . Extensive experiments have been conducted to demonstratethe effectiveness of UniGnn on multiple real-world datasets, which outperform the state-of-the-art approaches with a large margin . We further prove that the proposed message-passing based UniGsNN models are at most as powerful as the1-dimensional Generalized Weisfeiler-Leman (1-GWL) algorithm in terms of distinguishing non-isomorphic hypergraph . Our code is available at\url{https://://://http://://www.com/OneForward/UniGNN}.

Author(s) : Jing Huang, Jie Yang

Links : PDF - Abstract

Code :

https://github.com/doty-k/world_models


Coursera

Keywords : unignn - hypergraph - neural - unified - framework -

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