论文核心:由于图卷积网络根据邻接矩阵与当前节点相邻结点的信息更新当前节点,但是有些重要节点的信息不是一阶可达(first-order)的,比如句子中的重要单词和谓语动词不相邻,但是二者之间有重要联系,此时就需要多层GCN来捕捉间接信息,造成计算的复杂。本文提出一种结合邻接矩阵的加权图卷积模型,在该模型在依赖树中加入虚拟边...
论文地址:http://ecai2020.eu/papers/957_paper.pdf 代码地址:https://github.com/LILI-ZHOU/EA-WGCN 然而,现有的基于GCN的模型仅建立词与词之间的一阶( first-order)依赖关系,其效率和有效性受到限制。一些
However, Most GCN-based methods use the fixed graph topology. Besides, only a single-scale feature is used, and the multi-scale information is ignored. In this paper, we propose a multi-scale skeleton adaptive weighted graph convolution network (MS-AWGCN) for skeleton-based action recognition....
A Weighted GCN with Logical Adjacency Matrix for Relation Extraction (ECAI 2020) PyTorch implementation of Deep Learning approach for relation extraction task(SemEval 2010 Task 8 and TACRED) via a Weighted Graph Convolutional Neural Networks with Entity-Attention (EA-WGCN). This paper proposes a no...
论文地址 : http://ecai2020.eu/papers/957_paper.pdf论文源码 : https://github.com/LILI-ZHOU/EA-WGCN句子的形式化表示为 \chi = [x_1, x_2, \;...\; , x_i, \; ...\; , x_N ] , 其中 x_i 表示句子中第 i 个词, …
Graph convolutional network (GCN), with its capability to update the current node features according to the features of its first-order adjacent nodes and edges, has achieved impressive performance in dependency capturing. But some important nodes from which we should figure out the dependencies are...
wNAE根据邻接矩阵引入邻域结构信息,同时以加权方式保留自己的样本语义。 GCN 特定的结构表示由结构信息增强和监督,以减轻结构信息的消失。 wNAE 不仅可以用作表示学习的独立模块,还可以增强结构信息。在提出的 SEDCN 中,引入了 wNAE 来缓解 GCN 模块结构信息的消失。从 wNAE 模块学习到的 SES 表示以逐...
Concretely, we first utilize GCN to explore the local topological characteristics of each single snapshot and then employ LSTM to characterize the evolving features of the dynamic networks. Moreover, GAN is used to enhance the ability of the model to generate the next weighted network snapshot, ...
GETNext [9] and STHGCN [10] are transformer-based models that adopt graphical representations of trajectories. These models are unsuitable for our task since they focus on predicting the next POI in a trajectory rather than a set of POIs over a short period. High-order sequential relations ...
通过优化映射,学习空间中的几何关系反映了原始网络的结构。在这种情况下,已经开发出高效的网络嵌入技术,例如 DeepWalk [17]、Line [18]、图卷积网络 (GCN) [19] 和 GraphSAGE [20] 等。网络嵌入在深度学习中的概述可以是见[21]-[23]。 另一种网络嵌入方法是基于对复杂网络模型的研究。通过精心设计的机制,...