Results 11 to 20 of about 35,453 (267)
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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PLoS ONE, 2021 Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding ...
Jeongtae Son, Dongsup Kim
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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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Online social network user performance prediction by graph neural networks
IJAIN (International Journal of Advances in Intelligent Informatics), 2022 Online social networks provide rich information that characterizes the user’s personality, his interests, hobbies, and reflects his current state. Users of social networks publish photos, posts, videos, audio, etc. every day. Online social networks (OSN)
Fail Gafarov +2 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 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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Graph Convolutional Networks with Long-distance Words Dependency in Sentences for Short Text Classification [PDF]
Jisuanji kexue, 2022 With the wide application of graph neural network technology in the field of natural language processing,the research of text classification based on graph neural networks has received more and more attention.Building graph for text is an important ...
ZHANG Hu, BAI Ping
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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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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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