Results 11 to 20 of about 2,864 (219)
Node-Feature Convolution for Graph Convolutional Networks [PDF]
Pattern Recognition, 2022 Graph convolutional network (GCN) is an effective neural network model for graph representation learning. However, standard GCN suffers from three main limitations: (1) most real-world graphs have no regular connectivity and node degrees can range from one to hundreds or thousands, (2) neighboring nodes are aggregated with fixed weights, and (3) node ...
Zhang, L., Song, H., Aletras, N., Lu, H.
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Dynamic graph convolutional networks [PDF]
Pattern Recognition, 2020 Many different classification tasks need to manage structured data, which are usually modeled as graphs. Moreover, these graphs can be dynamic, meaning that the vertices/edges of each graph may change during time. Our goal is to jointly exploit structured data and temporal information through the use of a neural network model.
Franco Manessi +2 more
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Deformable Graph Convolutional Networks
Proceedings of the AAAI Conference on Artificial Intelligence, 2022 Graph neural networks (GNNs) have significantly improved the representation power for graph-structured data. Despite of the recent success of GNNs, the graph convolution in most GNNs have two limitations. Since the graph convolution is performed in a small local neighborhood on the input graph, it is inherently incapable to capture long-range ...
Jinyoung Park 0005 +3 more
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Graph-Revised Convolutional Network [PDF]
, 2021 Graph Convolutional Networks (GCNs) have received increasing attention in the machine learning community for effectively leveraging both the content features of nodes and the linkage patterns across graphs in various applications. As real-world graphs are often incomplete and noisy, treating them as ground-truth information, which is a common practice ...
Donghan Yu +4 more
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Lorentzian Graph Convolutional Networks [PDF]
Proceedings of the Web Conference 2021, 2021 Les réseaux convolutionnels de graphes (GCN) ont récemment fait l'objet d'une attention considérable de la part de la recherche. La plupart des GCN apprennent les représentations de nœuds en géométrie euclidienne, mais cela pourrait avoir une distorsion élevée dans le cas de l'intégration de graphes avec une structure sans échelle ou hiérarchique ...
Yiding Zhang +4 more
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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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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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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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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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