May 23, 2024 · Abstract page for arXiv paper 2405.14286: Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification.

Sep 25, 2024 · To tackle these limitations, we propose CoNHD, a new ENC solution that models both within-edge and within-node interactions as multi-input multi ...

This extension explicitly models both within-edge and within-node interactions as multi-input multi-output functions using two equivariant diffusion operators.

Jun 16, 2024 · In ENC, a node can have different labels across different hyperedges, which requires the modeling of node-hyperedge pairs instead of single ...

May 23, 2024 · The paper presents a novel approach to the challenging edge-dependent node classification task on hypergraphs.

Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification · no code implementations • 23 May 2024 • Yijia Zheng, Marcel Worring.

Co-Representation Neural Hypergraph Diffusion for Edge-Dependent Node Classification · Mining of Real-world Hypergraphs: Patterns, Tools, and Generators.

Aug 4, 2023 · We propose WHATsNet, a novel hypergraph neural network that represents the same node differently depending on the hyperedges it participates in.

Specifically, we first extend hypergraph diffusion using node-hyperedge co-representations. This extension explicitly models both within-edge and within-node ...

In this work, a universal feature encoder for both graph and hypergraph representation learning is designed, called UniG-Encoder.