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, cons
To address this limitation, this paper presents a Multi-Graph Structures and Hypergraph Convolutional Network (MGHCN) that combines diverse graphs and hypergraphs. The MGHCN simplifies the predictive framework by integrating key components that improve its robustness and accuracy. One of the most ...
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...
【2021/超图卷积】Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation 码农的科研笔记 个人公众号“码农的科研笔记” 4 人赞同了该文章 目录 收起 1 动机 2 方法 2.1【如何构建超图】 2.2【自监督增强模型】 2.3【损失函数】 3 总结 文章全文首发:码农的科研笔记(公...
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 ...
【论文阅读】Hypergraph Convolutional Network for Group RecommendationResponse status code does not indicate success: 404 (Not Found). 相关阅读:2.卷积神经网络(CNN) Xcode与Swift开发小记 我变秃了,也变强了——再探博客调优 Spring Bean 生命周期 (核心)(荣耀典藏版) 4.6版本Wordpress漏洞复现 超级...
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 Explore related subjects Discover the latest articles, news and stories from top researchers in ...
Hypergraph Convolutional Network for Group Recommendation (ICDM, 2021) [paper] THyMe+: Temporal Hypergraph Motifs and Fast Algorithms for Exact Counting (ICDM, 2021) [paper] Hypergraph Ego-networks and Their Temporal Evolution (ICDM, 2021) [paper] HyperTeNet: Hypergraph and Transformer-based Neural ...
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...