这篇论文投稿了 ICLR2021,被拒了,Multi-hop Attention Graph Neural Network 后来中了 IJCAI 2021 Multi-hop Attention Graph Neural Networks Motivation 现有的GAT等模型在一个layer中只能聚合邻居的信息,无法聚合更多的信息,GAT等模型通过堆叠多个layer的形式来实现【不
xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks This repository contains the source code and datasets for our paper: "xNeuSM: Explainable Neural Subgraph Matching with Graph Learnable Multi-hop Attention Networks". Requirements We use conda to manage the...
Contextualized Graph Attention Network for Recommendation with ItemKnowledge Graph Abstract 图神经网络(GNN)最近被应用于知识图谱辅助于推荐。 现有基于GNN的方法在KG中显式地建模实体与其局部图形上下文(一阶邻居集)之间的依赖关系,但可能无法有效地捕获其非局部图形上下文(即最相关的高阶邻居集)。本文提出...猜你...
Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension 论文解读:使用图注意力网络进行多粒度机器阅读理解的文档建模 阅读目的:学习该论文中基于文档结构的多粒度建模。 摘要 “自然问题”是一种具有挑战性的新机器阅读理解基准,它具有两个粒度的答案,即长答案(通常是一个段落...
In the large scale monitoring application of the IoT, the power carried by a single sensor node is limited, and it may not be replenished in the later stage. Therefore, it is required that the sensor nodes should save power when working, and the power co
Multihop self-attention mechanism; NLP: Natural language processing; PCNN: Piece-wise convolutional neural network; RNN: Recurrent neural network; SVM: Support vector machine Acknowledgements Authors would like to thank the editor and all anonymous reviewers for valuable suggestions and constructive comment...
Cao, Y., Fang, M., Tao, D.: BAG: bi-directional attention entity graph convolutional network for multi-hop reasoning question answering. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 20...
Hop-level attentionGraph Neural Networks (GNNs) have achieved state-of-the-art performance in graph-related tasks. Most of them pass messages between direct neighbors and the deeper GNNs can theoretically capture the more global neighborhood information. However, they often suffer from over-smoothing...
graph neural networkMulti-hop reading comprehension focuses on one type of factoid question, where a system needs to properly integrate multiple pieces of evidence to correctly answer a question. Previous work approximates global evidence with local coreference information, encoding coreference chains with...
To address these issues, we propose an innovative SSL framework for heterogeneous hypergraph embedding, expressly designed to enhance graph-level classification. Our framework introduces multi-hop attention in hypergraph convolution, a significant leap from existing attention mechanisms specifically for ...