Results 21 to 30 of about 5,438,478 (196)
AM-GCN: Adaptive Multi-channel Graph Convolutional Networks [PDF]
Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020 Graph Convolutional Networks (GCNs) have gained great popularity in tackling various analytics tasks on graph and network data. However, some recent studies raise concerns about whether GCNs can optimally integrate node features and topological structures in a complex graph with rich information.
Xiao Wang 0017 +5 more
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Spatial Autocorrelation Analysis with Graph Convolutional Neural Network
, 2021 Spatial autocorrelation statistics have a long-standing history being used by geographers todetermine whether identifiable spatial patterns exist in data.
Pengyuan Liu (32609) +1 more
core +6 more sources
Graph convolutional networks fusing motif-structure information
Scientific Reports, 2022 With the advent of the wave of big data, the generation of more and more graph data brings great pressure to the traditional deep learning model. The birth of graph neural network fill the gap of deep learning in graph data.
Bin Wang +4 more
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IA-GCN: Interactive Graph Convolutional Network for Recommendation
CoRR, 2022 Recently, Graph Convolutional Network (GCN) has become a novel state-of-art for Collaborative Filtering (CF) based Recommender Systems (RS). It is a common practice to learn informative user and item representations by performing embedding propagation on a user-item bipartite graph, and then provide the users with personalized item suggestions based on
Zhang, Yinan +9 more
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Sensors, 2023 Graph convolutional neural network architectures combine feature extraction and convolutional layers for hyperspectral image classification. An adaptive neighborhood aggregation method based on statistical variance integrating the spatial information ...
Jairo Orozco +4 more
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Geom-GCN: Geometric Graph Convolutional Networks
CoRR, 2020 Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to ...
Hongbin Pei +4 more
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Applied Sciences, 2021 In this paper, we address the problem of autonomous exploration in unknown environments for ground mobile robots with deep reinforcement learning (DRL). To effectively explore unknown environments, we construct an exploration graph considering historical
Zhiwen Zhang +4 more
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LGD-GCN: Local and Global Disentangled Graph Convolutional Networks
CoRR, 2021 add additinal code and paper ...
Jingwei Guo 0001 +3 more
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IEEE Access, 2022 Graph Convolutional Network (GCN) has achieved significant success in many graph representation learning tasks. GCN usually learns graph representations by performing Neighbor Aggregation (NA) and Feature Transformation (FT) operations.
Dezhi Sun, Man Hu, Zhenyu Li
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InducT-GCN: Inductive Graph Convolutional Networks for Text Classification
2022 26th International Conference on Pattern Recognition (ICPR), 2022 Text classification aims to assign labels to textual units by making use of global information. Recent studies have applied graph neural network (GNN) to capture the global word co-occurrence in a corpus. Existing approaches require that all the nodes (training and test) in a graph are present during training, which are transductive and do not ...
Kunze Wang +2 more
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