Heterogeneous Graph Neural Network heterogeneous-network network-embedding graph-neural-network heterogeneous-graph graph-attention-network heterogeneous-graph-neural-network Updated May 6, 2020 Python Jhy1993 / Representation-Learning-on-Heterogeneous-Graph Star 436 Code Issues Pull requests Representation...
To address this problem, in this paper we incorporate enhanced representations into a gated graph convolutional network to enrich the background information and further improve the attention mechanism to focus on the most relevant relation. Specifically, in the proposed framework, 1) we introduce a ...
3, the academic coauthor heterogeneous network is converted into a homogeneous network which is composed of author nodes. Show abstract MRGAT: Multi-Relational Graph Attention Network for knowledge graph completion 2022, Neural Networks Show abstract A gated graph convolutional network with multi-sensor...
(GTN) to learn the correlation of the heterogeneous multimodal data and integrates a Graph Attention Network (GAT) with Gated Recurrent Unit (GRU) to ... C Zhao,M Ma,Z Zhong,... 被引量: 0发表: 2023年 Deep Heuristic Evolutionary Regression Model Based on the Fusion of BiGRU and BiLSTM...
Dualgats: Dual graph attention networks for emotion recognition in conversations. In ACL, pages 7395–7408, 2023. [58] Yonggang Zhang, Mingming Gong, Tongliang Liu, Gang Niu, Xinmei Tian, Bo Han, Bernhard Schölkopf, and Kun Zhang. Causaladv: Adversarial robustnes...
At runtime, the computation at each layer is represented as a dataflow graph, and the com- position of those dataflow graphs forms the global dataflow graph for the entire computation. The clus- ter execution engine manages the distributed aspect of the execution and is responsible for cluster ...
3D liver tumor segmentation in CT images using improved fuzzy C-means and graph cuts. BioMed Research International, 2017 Christ P, Ettlinger F, Grün F, Elshaer M, Lipková J, Schlecht S, Ahmaddy F, Tatavarty S, Bickel M, Bilic P, Rempfler M, Hofmann F, DAnastasi M, Ahmadi S, ...
To address this problem, in this paper we incorporate enhanced representations into a gated graph convolutional network to enrich the background information and further improve the attention mechanism to focus on the most relevant relation. Specifically, in the proposed framework, 1) we introduce a ...
STAMP replaces RNNs in previous work with multiple attention layers, and captures the short-term interest of the user through the last item in the current session. GNNs: SR-GNN constructs the session sequence as a graph, and uses gated GNNs to acquire the encoded representation of items. ...
heterogeneous graph; multi-head attention; multi-feature interaction; meta-path aggregation; heterogeneous graph neural network1. Introduction Graph structure data [1] are widely used to represent a variety of practical application problems, including social networks [2,3], physical systems [4,5], ...