Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence 2019
DOI: 10.24963/ijcai.2019/366
|View full text |Cite
|
Sign up to set email alerts
|

Dynamic Hypergraph Neural Networks

Abstract: 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 directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC).… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1
1

Citation Types

0
107
0

Year Published

2020
2020
2021
2021

Publication Types

Select...
5
4
1

Relationship

1
9

Authors

Journals

citations
Cited by 265 publications
(107 citation statements)
references
References 7 publications
0
107
0
Order By: Relevance
“…Admittedly, the attention to higher-order systems has proliferated lately 5,6,8,[10][11][12][13][14][15][16][17][18][19][20] , with an increasing focus on hypergraphs in fields such as mathematics 6,12,13,15,16,21,22 , physics 8,10,11,[17][18][19][23][24][25] , and computer science [26][27][28][29][30][31][32][33][34] . In many fields, the dynamical systems approach appears naturally.…”
mentioning
confidence: 99%
“…Admittedly, the attention to higher-order systems has proliferated lately 5,6,8,[10][11][12][13][14][15][16][17][18][19][20] , with an increasing focus on hypergraphs in fields such as mathematics 6,12,13,15,16,21,22 , physics 8,10,11,[17][18][19][23][24][25] , and computer science [26][27][28][29][30][31][32][33][34] . In many fields, the dynamical systems approach appears naturally.…”
mentioning
confidence: 99%
“…[22] presented a graph convolution network training method in a hypergraph, which updated the network's nodes through the graph convolution. [23] proposed a dynamic hypergraph neural neural networks framework to tackle the issue that hidden and important relations are not directly represented in the inherent structure. It is composed of two modules: dynamic hypergraph construction(DHG) and hypergraph convolution(HGC).…”
Section: C. Deep Learning Based Methodsmentioning
confidence: 99%
“…Despite this modification, the node representations are still computed as in Equations 1 and 2. It is possible to have also time-varying hypergraphs [38].…”
Section: Graph-wise Learning Objectivesmentioning
confidence: 99%