In this paper, we propose SR-HetGNN, a novel session recommendation method that uses a heterogeneous graph neural network (HetGNN) to learn session embeddings and capture the specific preferences of anonymous u
【论文解读 KDD 2019 | HetGNN】Heterogeneous Graph Neural Network,程序员大本营,技术文章内容聚合第一站。
Heterogeneous Graph Neural Network 背景介绍 文章核心思想? 文章针对异构图网络进行建模,得到每个节点的向量表示。首先,利用基于重启的随机游走策略为每个节点根据节点类型选择邻居,然后利用两个模块聚合邻居节点特征:一方面,对节点的不同类型特征进行建模,生成特征向量;另一方面,聚合不同类型的邻居节点,并融合注意力机制,...
因此,作者提出了HetGNN模型来解决此事。 作者认为当前工作对于异质图还没有解决好的三个问题: 异质图中的大多数节点并不会连接所有类型的其他节点。如academic graph中user节点不会直接连到venue节点上。另外说节点能够连接的邻居数也不一样。大部分GNN直接聚合邻居节点信息,而远处传过来的节点信息会随着距离而减弱。
论文笔记:KDD 2019 Heterogeneous Graph Neural Network /KDD2019_HetGNN该文提出了一种基于深层模型的异质网络表示学习的方法HetGNN。异构图的表示学习旨在为每个节点寻求一个有意义的向量表示,以便于链接预测、个性化推荐、节点分类等下游应用。然而...端方式训练模型。 2.异质图神经网络异质图定义为包含多类节点或多类...
Heterogeneous Graph Neural Network 背景介绍 文章核心思想? 文章针对异构图网络进行建模,得到每个节点的向量表示。首先,利用基于重启的随机游走策略为每个节点根据节点类型选择邻居,然后利用两个模块聚合邻居节点特征:一方面,对节点的不同类型特征进行建模,生成特征向量;另一方面,聚合不同类型的邻居节点,并融合注意力机制,...
Heterogeneous Graph Neural Network(HetGNN) (a) The overall architecture of HetGNN(b) NN-1: node heterogeneous contents encoder; (c) NN-2: type-based neighbors aggregator; (d) NN-3: heterogeneous types combination. 31 Outline l Models ¢ Shallow models ¢ Deep models √ Meta-path selec...
HetGNN (Zhang, Song, et al., 2019b) is a heterogeneous graph neural network method that utilizes random walks with restart. This strategy allows for the sampling of a consistent number of closely related neighbors for each node in a heterogeneous graph. Subsequently, it organizes these neighbors...
Yu L, Sun L, Du B, Zhu T, Lv W (2022) Label-enhanced graph neural network for semi-supervised node classification. IEEE Trans Knowl Data Eng Guan M, Cai X, Shang J, Hao F, Liu D, Jiao X, Ni W (2023) Hmsg: heterogeneous graph neural network based on metapath subgraph learning...
In this work, a novel framework, termed Multivariate Time Series Forecasting with Heterogeneous Graph Neural Network (MTHetGNN) is proposed and applied for the MTS forecasting task. MTHetGNN embeds each relation or interdependency into each graph structure and fuses all graph structures with temporal ...