Heterogeneous graphs can accurately and effectively model rich semantic information and complex network relationships in the real world. As a deep representation model for nodes, heterogeneous graph neural networks (HGNNs) offer powerful graph data processing capabilities and exhibit outstanding performance ...
Extensive experiments on three real-world heterogeneous graph data demonstrate that the proposed HetGTCN and HetGTAN are efficient and consistently outperform all state-of-the-art HGNN baselines on semi-supervised node classification tasks, and can go deep without compromising performance. PDF Abstract...
[55] introduced contrastive learning into an HGNN and proposed a model called HeCo for learning node embedding. Show abstract OSGNN: Original graph and Subgraph aggregated Graph Neural Network 2023, Expert Systems with Applications Citation Excerpt : Metapath extracted graph neural network (MEGNN) (...
论文名称:Heterogeneous Graph Neural Network via Attribute Completion论文链接:链接异质信息网络(HINs)也称为异质图,它是一种由多种类型的节点和边组成的复杂网络,包含了全面的信息和丰富的语义。图神经网络(GNNs)作为处理图结构数据的强大工具,在网络分析任务中表现出了卓越的性能。最近相继提出了许多基于图神经网络...
2.1.3 Heterogeneous Graph Encoding 这里是借鉴了HGNN网络结构(conventional homogeneous graph neural network )(Kipf and Welling 2017) l层的 每个结点的隐层表示为Hl HGNN会考虑各种类型的节点,并将它们与它们各自的可训练矩阵映射到一个隐层公共空间。
Heterogeneous graph neural networks (HGNNs) have the powerful capability to embed rich structural and semantic information of a heterogeneous graph into node representations. Existing HGNNs inherit many mechanisms from graph neural networks (GNNs) designed for homogeneous graphs, especially the attention ...
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...
BUPT-GAMMA/OpenHGNN Star868 This is an open-source toolkit for Heterogeneous Graph Neural Network(OpenHGNN) based on DGL. pytorchheterogeneousgraph-neural-networksdgl UpdatedNov 14, 2024 Python BabitMF/bmf Star804 Cross-platform, customizable multimedia/video processing framework. With strong GPU accel...
KDD 2021 《Pre-training on Large-Scale Heterogeneous Graph》 问题:自监督的目的是从无标签,网络结构(一般是同构图)中抽取transferable knowledge;与实际系统有gap,实际系统一般是异构图。 方法:PT-HGNN。 一个对比学习的策略来捕获异构的语义和结构信息。节点级和架构级 node- and schema-level。保持异构的语义和...
Our paper [ OpenHGNN: An Open Source Toolkit for Heterogeneous Graph Neural Network ](https://dl.acm.org/doi/abs/10.1145/3511808.3557664) is accpeted at CIKM 2022 short paper track. 2022-06-27 release v0.3 We release the latest version v0.3. New models API Usage Simply customization of ...