Hypergraph

What's the Next of Hypergraph Neural Networks Framework?

Here we define a general data structure for high-order learning. To Be Continue...

Dual Channel Hypergraph Collaborative Filtering

Collaborative filtering (CF) is one of the most popular and important recommendation methodologies in the heart of numerous recommender systems today. Although widely adopted, existing CF-based methods, ranging from matrix factorization to the …

MICCAI 2019: A Tutorial on Hypergraph Neural Network Toolbox

We organize a tutorial about "Hypergraph Learning: Methods, Tools and Applications in Medical Image Analysis". I give a talk about "A Tutorial on Hypergraph Neural Network Toolbox".

Dynamic Hypergraph Neural Networks

In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not …

Emotion Recognition by Edge-Weighted Hypergraph Neural Network

Over the past decade, increasing research efforts have been concentrated on emotion recognition from physiological signals due to their capability on emotion information representation. Existing works mainly focus on exploring the relationship …

Hypergraph Neural Networks

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 …

Physiological Signals-based Emotion Recognition via High-order Correlation Learning

Emotion recognition by physiological signals is an effective way to discern the inner state of human beings and therefore has been widely adopted in many user-centered applications. The majority of current state-of-the-art methods focus on exploring …