[5]. One key aspect in GNN is to define the convolutional operator in the graph domain. Bruna et al. [19] firstly define convolution in the Proposed approach In this section, we first give the definition of hyp
In view of this, the study takes the hypergraph convolutional network under deep learning as the framework basis, optimizes the performance by introducing the self-attention module and the topology module, constructs the temporal refinement channel and the channel refinement channel, and adds the ...
论文题目:A Hypergraph Convolutional Neural Network for Molecular Properties Prediction using Functional Group 发表年份: 会议/期刊名: ABSTRACT 提出目前存在的问题 传统的基于图的方法考虑了节点间的成对交互,不能灵活地表达图中多个节点之间的复杂关系,并且应用多条可能会导致过渡平滑和过拟合问题 本文方法和创新...
DHCN:Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation 摘要:在交互序列中进行特征捕捉对用户的兴趣建模无疑是重要的,但目前的研究大都停留在项目对上的关系特征捕捉,而忽略了序列中项目间的高阶信息。而超图的性质能够很好地解决这个问题。 超图定义 超图的定义:一条超边e可以连接多个...
Attentive Graph Neural Networks for Holistic Sequential Recommendation 5.Self-Supervised Multi-Channel Hypergraph...Self-Supervised Multi-Channel Hypergraph ConvolutionalNetwork for Social Recommendation ? 1.8K00 Brief Bioinform|基于动态超图对比学习的多关系药物-基因相互作用预测 ...
A Mixed Hypergraph Convolutional Network for Session-Based Recommendation Chapter © 2024 Exploiting Item Relationships with Dual-Channel Attention Networks for Session-Based Recommendation Chapter © 2023 Data Availability The data generated during and/or analysed during the current study is available...
[55] proposed 1.5 Hypergraph Computation: Challenges and Objectives 11 HyperGCN to train a GCN for semi-supervised learning on hypergraphs using graph convolutional networks (GCNs). As for the spatial-based method, by extending the dynamic hypergraph learning, Jiang et al. [56] proposed a dynamic...
NeurIPS 2019: HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs semi-supervised-learninghypergraphgraph-convolutional-network UpdatedAug 16, 2020 Python Mt-KaHyPar (Multi-Threaded Karlsruhe Hypergraph Partitioner) is a shared-memory multilevel graph and hypergraph partitioner equi...
This paper proposes a dynamic spatial-temporal hypergraph convolutional network (DST-HCN) to capture spatial-temporal information for skeleton-based action recognition. DST-HCN introduces a time-point hypergraph (TPH) to learn relationships at time points. With multiple spatial static hypergraphs and ...
In this paper, we propose a novel weighted hypergraph convolutional network-based method, called WHCN, to confront the challenges of learning point-wise labels from scene-level annotations. Firstly, in order to simultaneously overcome the point imbalance among different categories and reduce the model...