Graph neural networks (GNNs) are able to achieve state-of-the-art performance for node
representation and classification in a network. But, most of the existing GNNs can be applied …

Network representation learning and node classification in graphs got significant attention
due to the invent of different types graph neural networks. Graph convolution network (GCN) …

Hypergraph, an expressive structure with flexibility to model the higher-order correlations
among entities, has recently attracted increasing attention from various research domains …

Hypergraphs, with hyperedges connecting more than two nodes, are key for modelling
higher-order interactions in real-world data. The success of graph neural networks (GNNs) …

Recently, graph neural networks have achieved great success on the representation
learning of the graph-structured data. However, these networks just consider the pairwise …

As a powerful tool for modeling complex relationships, hypergraphs are gaining popularity
from the graph learning community. However, commonly used frameworks in deep …

Hypergraphs, with their capacity to depict high-order relationships, have emerged as a
significant extension of traditional graphs. Although Graph Neural Networks (GNNs) have …

Hypergraphs provide a natural representation for many real world datasets. We propose a
novel framework, HNHN, for hypergraph representation learning. HNHN is a hypergraph …

Recently, graph neural networks have attracted great attention and achieved prominent
performance in various research fields. Most of those algorithms have assumed pairwise …

Graph representation learning for hypergraphs can be used to extract patterns among
higher-order interactions that are critically important in many real world problems. Current …