Results 11 to 20 of about 38,896 (265)
Progressive Graph Convolutional Networks for Semi-Supervised Node Classification
IEEE Access, 2021 Graph convolutional networks have been successful in addressing graph-based tasks such as semi-supervised node classification. Existing methods use a network structure defined by the user based on experimentation with fixed number of layers and neurons ...
Negar Heidari, Alexandros Iosifidis
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Simple Graph Convolutional Networks
CoRR, 2021 Many neural networks for graphs are based on the graph convolution operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, that tend to add complexity (and non-linearity) to the model. In this paper, we follow the opposite direction by proposing simple graph convolution operators, that can be implemented ...
Luca Pasa +3 more
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Hybrid Graph Models for Traffic Prediction
Applied Sciences, 2023 Obtaining accurate road conditions is crucial for traffic management, dynamic route planning, and intelligent guidance services. The complex spatial correlation and nonlinear temporal dependence pose great challenges to obtaining accurate road conditions.
Renyi Chen, Huaxiong Yao
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Epidemic Graph Convolutional Network [PDF]
Proceedings of the 13th International Conference on Web Search and Data Mining, 2020 A growing trend recently is to harness the structure of today's big data, where much of the data can be represented as graphs. Simultaneously, graph convolutional networks (GCNs) have been proposed and since seen rapid development. More recently, due to the scalability issues that arise when attempting to utilize these powerful models on real-world ...
Tyler Derr +5 more
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Mutual teaching for graph convolutional networks [PDF]
Future Generation Computer Systems, 2021 GCN, 8 pages, 1 ...
Kun Zhan, Chaoxi Niu
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Graph Convolutional Networks with EigenPooling [PDF]
Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2019 Graph neural networks, which generalize deep neural network models to graph structured data, have attracted increasing attention in recent years. They usually learn node representations by transforming, propagating and aggregating node features and have been proven to improve the performance of many graph related tasks such as node classification and ...
Yao Ma 0001 +3 more
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Tangent Graph Convolutional Network
ESANN 2021 proceedings, 2021 Most Graph Convolutions (GCs) proposed in the Graph Neural Networks (GNNs) literature share the principle of computing topologically enriched node representations based on the ones of their neighbors. In this paper, we propose a novel GNN named Tangent Graph Convolutional Network (TGCN) that, in addition to the traditional GC approach, exploits a novel
luca pasa +2 more
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Multi-Semantic Alignment Graph Convolutional Network
Connection Science, 2022 Graph Convolutional Network (GCN) is a powerful emerging deep learning technique for learning graph data. However, there are still some challenges for GCN. For example, the model is shallow; the performance is poor when labelled nodes are severely scarce.
Jisheng Qin +3 more
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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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Spiking Graph Convolutional Networks
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022 Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices.
Zulun Zhu +5 more
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