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...
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}^...
Specifically, we first design a heterogeneous graph for the text corpus, and then apply HeGTM on the constructed text graph to learn better text representations that contain various semantic relations. In addition, our approach can also be used as a strong meta-structure extractor for other GNN ...
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 ...
Graph embedding, aiming to learn low-dimensional representations of nodes while preserving valuable structure information, has played a key role in graph analysis and inference. However, most existing methods deal with static homogeneous topologies, whil
negative transfer第一次看到,标记一下。To transfer or not to transfer. In Advances in Neural Information Processing Systems (NeurIPS), Workshop or transfer learning, volume 898, pp. 1–4, 2005. 这篇工作聚焦的点是分子化合物的graph识别。引入图卷积和readout函数。node-level提出了两个任务:context ...
Heterogeneous Graph Attention Networks for Semi-supervised Short Text Classification[EMNLP 2019] Heterogeneous Graph Structure Learning for Graph Neural Networks[AAAI 2021] Self-supervised Heterogeneous Graph Neural Network with Co-contrastive Learning[KDD 2021] ...
In this work, we propose an explainable Heterogeneous Graph Reasoning (HGR) model to unify the relational dialogue context understanding and entity-correlation reasoning into a heterogeneous graph structure. HGR encodes entity context according to the corresponding utterance and deduces next response after...
Local Structure Encoder Training objective Experiments Keywords: Graph Transformer, Heterogeneous Information Networks https://arxiv.org/abs/2302.11329arxiv.org/abs/2302.11329 Introduction 异构信息网络(HINs)是一种将具有多种类型的节点和边连接起来的结构,这意味着节点之间存在复杂的异构语义关系。由于许多...