Hypergraph Neural Networks

Understand

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.

• Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data.
• In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning.
• In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently.