Results 31 to 40 of about 3,278,052 (298)
Factorizable Graph Convolutional Networks
CoRR, 2020 Graphs have been widely adopted to denote structural connections between entities. The relations are in many cases heterogeneous, but entangled together and denoted merely as a single edge between a pair of nodes. For example, in a social network graph, users in different latent relationships like friends and colleagues, are usually connected via a ...
Yiding Yang +3 more
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Structural reinforcement-based graph convolutional networks
Connection Science, 2022 Graph Convolutional Network (GCN) is a tool for feature extraction, learning, and inference on graph data, widely applied in numerous scenarios. Despite the great success of GCN, it performs weakly under some application conditions, such as a multiple ...
Jisheng Qin, Qianqian Wang, Tao Tao
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Adaptive Propagation Graph Convolutional Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2021 Graph convolutional networks (GCNs) are a family of neural network models that perform inference on graph data by interleaving vertex-wise operations and message-passing exchanges across nodes. Concerning the latter, two key questions arise: (i) how to design a differentiable exchange protocol (e.g., a 1-hop Laplacian smoothing in the original GCN ...
Spinelli I, Scardapane S, Uncini A
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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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Spatio-Temporal Joint Graph Convolutional Networks for Traffic Forecasting
, 2023 Recent studies have shifted their focus towards formulating traffic forecasting as a spatio-temporal graph modeling problem. Typically, they constructed a static spatial graph at each time step and then connected each node with itself between adjacent ...
Philip S. Yu +15 more
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Review of Node Classification Methods Based on Graph Convolutional Neural Networks [PDF]
Jisuanji kexueNode classification is one of the important research tasks in graph field.In recent years,with the continuous deepening of research on graph convolutional neural network,significant progress has been made in the research and application of node ...
ZHANG Liying, SUN Haihang, SUN Yufa , SHI Bingbo
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Signed Graph Convolutional Networks
2018 IEEE International Conference on Data Mining (ICDM), 2018 Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). They have been shown to provide a significant improvement on a
Tyler Derr, Yao Ma 0001, Jiliang Tang
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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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Directed Graph Convolutional Network
CoRR, 2020 Graph Convolutional Networks (GCNs) have been widely used due to their outstanding performance in processing graph-structured data. However, the undirected graphs limit their application scope. In this paper, we extend spectral-based graph convolution to directed graphs by using first- and second-order proximity, which can not only retain the ...
Zekun Tong +4 more
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Masked Graph Convolutional Network [PDF]
Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, 2019 Semi-supervised classification is a fundamental technology to process the structured and unstructured data in machine learning field. The traditional attribute-graph based semi-supervised classification methods propagate labels over the graph which is usually constructed from the data features, while the graph convolutional neural networks smooth ...
Liang Yang 0002 +4 more
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