Hypergraph Neural Networks HGCN与MARL Efficient Cooperation Strategy Generation in Multi-Agent Video Games via Hypergraph Neural Network Value Function Factorisation with Hypergraph Convolution for Cooperative Multi-agent Reinforcement Learning PyG库中HypergraphConv类的使用 PyG库的官方资料 HypergraphConv类的用法...
Survey of Hypergraph Neural Networks and Its Application to Action Recognition (CAAI International Conference on Artificial Intelligence, 2022) [paper] A Survey on Various Representation Learning of Hypergraph for Unsupervised Feature Selection (Data, Engineering and Applications, 2022) [paper] Hypergraph L...
Hypergraph Neural Networks (AAAI 2019) deep-learningpytorchhypergraphcomplex-datahypergraph-neural-networks UpdatedAug 31, 2022 Python iMoonLab/DeepHypergraph Star669 Code Issues Pull requests Discussions A pytorch library for graph and hypergraph computation. ...
超图神经网络(Hypergraph Neural Nerworks,HGNN) 1. 超图学习(Hypergraph Learning) 在本节中我们简单回顾超图的定义及常见性质。 1.1 什么是超图 超图与常见的简单图不同。对于一个简单图,其每条边均与两个顶点相关联,即每条边的度都被限制为2。而超图则允许每一条边的度为任何非负整数。超图的严格数学定义如下...
[LG] Molecular Hypergraph Neural Networks O网页链接 分子超图神经网络(MHNN)是一种针对有机半导体的光电性能预测的模型。MHNN通过引入分子超图来解决传统图模型无法准确表示多中心化合物键和共轭结构等高阶连接的问题。该模型在多个有机光伏相关数据集上表现出色,即使没有使用任何三维几何信息,也能超越基线模型的性能...
本文提出动态超图神经网络框架(DHGNN)，旨在解决神经网络模型中深层生成嵌入特征在捕获高阶关系方面的局限性。该框架由动态超图构建(DHG)和超图卷积(HGC)两部分构成。HGC模块包括顶点卷积与超边缘卷积，分别负责聚合顶点与超边特征。DHGNN的关键贡献在于动态超图构建(DHG)与超图卷积(HGC)。DHG通过欧氏距离...
Hypergraph Neural Networks (HGCN) are a powerful tool in various applications, particularly in multi-agent systems. They enable efficient cooperation strategy generation through hypergraph structures in video games, as demonstrated in papers like "Efficient Cooperation Strategy Generation in ...
graph neural networkconvolutional neural networkHypergraph neural networks (HGNNs), extending the techniques of graph neural networks, have been applied to various fields due to their ability to capture more complex high-order node relationships. However, for hyperspectral image (HSI) classification ...
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...
Distributed Constrained Combinatorial Optimization leveraging Hypergraph Neural Networks Abstract Scalable addressing of high dimensional constrained combinatorial optimization problems is a challenge that arises in several science and engineering disciplines. Recent work introduced novel application of graph neural ...