Hypergraph Theory: An Introduction

By A. Bretto. 2013

615 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/1206827c5969ee8a7501c66e0cbf4a09ed8216b8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Li-zhen Cui
2. Nguyen Quoc Viet Hung
3. Jundong Li
4. Xin Xia
5. Qinyong Wang
6. Hongzhi Yin
7. Junliang Yu
8. Xiangliang Zhang

Citations in the corpus

• Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation (2021, background): “Hypergraph [4], which generalizes the concept of edge to make it connect more than two nodes, provides a natural way to model complex high-order relations among users.”
• Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation (2020, background): “Conceptually, a hypergraph (Bretto 2013) is composed of a vertex set and a hyperedge set, where a hyperedge can connect any numbers of vertices, which can be used to encode high-order data correlations.”