Existing survey papers of heterogeneous graph representation learning summarize all possible embedding techniques for graphs and make insufficient analysis for deep neural network models. To tackle this issue, i
Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications Rui Bing, Guan Yuan, Mu Zhu, Fanrong Meng, Huifang Ma, Shaojie Qiao 2022 PHGNN: Position-aware Graph Neural Network for Heterogeneous Graph Embedding ...
To study a general heterogeneous graph embedding model framework, we explore how to generate node embeddings in heterogeneous graphs by a graph neural network without using a meta-path. However, it is not easy to generate node embeddings in heterogeneous graphs by graph neural networks without ...
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,...
与直接融合邻居属性更新节点嵌入的图神经网络(graph neural network, GNNs)不同,由于节点和边的类型不同,HGNNs需要克服属性的异质性,设计有效的融合方法来利用邻居信息,这带来了更多的挑战。在本节中,我们将异构网络表示分为无监督和半监督。 3.2.1无监督神经网络(是为了学习有良好泛化能力的节点嵌入, 他们总是...
目录 前言 方法分类 Benchmark 前言 原文链接:Heterogeneous Network,还有一篇Survey也挺好的,可以参照,HGT作者写的:Another Survey(更侧重于Attention的解释) 文章的几个亮点:1. 系统的对常见的异构图进行分类,并提供了较为统一的范式助于理解 2. 创建了四个Benchmark数据集进行异构图性能的统一比较 3. 提供了分析...
Graph Neural Networks 8 § Deep model § Apply deep neural network for graph § Autoencoder approaches: e.g., DNGR and SDNE § GNN based approaches § Average neighbor information and apply a neural network § e.g., GCN, GraphSage, GAT 9 HeterogeneousInformation Networks l Heterogeneous Inform...
A survey of graph neural network based recommendation in social networks 2023, Neurocomputing Show abstract Neighbor enhanced graph convolutional networks for node classification and recommendation 2022, Knowledge-Based Systems Show abstract An explainable recommendation framework based on an improved knowledge...
Liu T, Zhang J (2023) An adaptive traffic flow prediction model based on spatiotemporal graph neural network. J Supercomput 79(14):15245–15269 Article MATH Google Scholar Gao C, Zheng Y, Li N, Li Y, Qin Y, Piao J, Quan Y, Chang J, Jin D, He X (2023) A survey of graph neu...
many sequence- and/or structure- and graph-based computational approaches often ignore either the topological information in NPIs or the influence of other molecule networks on NPI prediction. In this work, we propose NPI-HGNN, an end-to-end graph neural network (GNN)-based approach for the id...