Heterogeneous Graph Structure Learning for Graph Neural Networks (HGSL)论文笔记,程序员大本营,技术文章内容聚合第一站。
To solve these problems, we propose a novel model named Heterogeneous Graph Structure Learning Based on Feature and Topology Information Extraction (HGSL-FTIE) . First, we propose an n-hop meta-path extraction strategy. Based on this strategy, we fuse the node features to construct the feature...
Zhao J, Wang X, Shi C, Hu B, Song G, Ye Y (2021) Heterogeneous graph structure learning for graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4697–4705 Zhang C, Song D, Huang C, Swami A, Chawla NV (2019) Heterogeneous graph ne...
Heterogeneous Graph Structure Learning for Graph Neural Networks Jianan Zhao, Xiao Wang, C. Shi, Binbin Hu, Guojie Song, Yanfang Ye 2021 ? How to read the graph Each node is an academic paper related to the origin paper. Papers are arranged according to ...
2021. Heterogeneous Graph Structure Learning for Graph Neural Networks. In AAAI. 问题定义 图$$G={\{V,E}\}$$, V 和E 分别是节点集和边集,边集中的每条边表示节点之间的连接。 定义1: 属性多路异构网络,简称AMHEN。对给定的图G,本文进一步将节点集 V 中的所有节点与属性特征向量 X\in{\Bbb{R}^...
Heterogeneous graphs Contrastive learning Homogeneous subgraphs Graph neural network Attention mechanism Meta-path Meta-structure 1. Introduction Network representation learning, a technique used in machine learning (Kiaghadi & Hoseinpour, 2023) and data science (Liu et al., 2023), involves learning mea...
Looking at the OGB leaderboard, common ways for learning knowledge graph embeddings like ComplEx or even DistMult, which are available out of the box in something like DGL-KE, seem to do well. Our approach appears to be to learn the graph structure through both learnable node embeddings and ...
Heterogeneous Graph Structure Learning for Graph Neural Networks (HGSL)论文笔记 目标:异质网络(子图)的嵌入,用于节点分类任务 方法:根据边(R)类型的的不同分解成多个子网络,对每个子网络使用GCN,进行联合优化,目标为是最小化交叉熵(目标是节点分类) 具体流程如图: 一、得到关系r1的子图 对于一类关系 r1 ,指...
Since graph is a special manifold structure that does not possess translation invariance property, traditional convolutional neural networks (CNNs) cannot be directly used on graphs. Show abstract CGAAD: Centrality- and Graph-Aware Deep-Learning Model for Detecting Cyberattacks Targeting Industrial ...
More and more scholars pay attention to this research because heterogeneous graph representation learning shows strong application potential in real-world scenarios. However, the existing heterogeneous graph models use data augmentation techniques to enhance the use of graph structure information, which only...