Results 41 to 50 of about 3,278,052 (298)

Two-way Feature Augmentation Graph Convolution Networks Algorithm [PDF]

Jisuanji kexue
Graph convolutional neural network algorithms play a crucial role in the processing of graph structured data.The mainstream mode of existing graph convolutional networks is based on weighted summation of node features using Laplacian matrices,with a ...
LI Mengxi, GAO Xindan, LI Xue
doaj   +1 more source

Dual graph convolutional neural network for predicting chemical networks

BMC Bioinformatics, 2020
Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery.
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima   +6 more
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Spatial Graph Convolutional Networks [PDF]

, 2020
Graph Convolutional Networks (GCNs) have recently become the primary choice for learning from graph-structured data, superseding hash fingerprints in representing chemical compounds. However, GCNs lack the ability to take into account the ordering of node neighbors, even when there is a geometric interpretation of the graph vertices that provides an ...
Tomasz Danel, Przemyslaw Spurek, Jacek Tabor, Marek Smieja, Lukasz Struski, Agnieszka Slowik, Lukasz Maziarka   +6 more
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A deep graph convolutional neural network architecture for graph classification.

PLoS ONE, 2023
Graph Convolutional Networks (GCNs) are powerful deep learning methods for non-Euclidean structure data and achieve impressive performance in many fields. But most of the state-of-the-art GCN models are shallow structures with depths of no more than 3 to
Yuchen Zhou, Hongtao Huo, Zhiwen Hou, Fanliang Bu   +3 more
doaj   +1 more source

Geometric Deep Learning for Protein–Protein Interaction Predictions

IEEE Access, 2022
This work introduces novel approaches, based on geometrical deep learning, for predicting protein–protein interactions. A dataset containing both interacting and non-interacting proteins is selected from the Negatome Database.
Gabriel St-Pierre Lemieux, Eric Paquet, Herna L Viktor, Wojtek Michalowski   +3 more
doaj   +1 more source

Node-attribute graph layout for small-world networks [PDF]

, 2011
Small-world networks are a very commonly occurring type of graph in the real-world, which exhibit a clustered structure that is not well represented by current graph layout algorithms. In many cases we also have information about the nodes in such graphs,
Joe Faith, Helen Gibson, Gibson, Helen, Faith, Joe   +3 more
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SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks [PDF]

, 2021
Graph convolutional networks have been successfully applied in various graph-based tasks. In a typical graph convolutional layer, node features are updated by aggregating neighborhood information.
Xu, M, Zhang, G, Guoqiang Zhang, Kenta Niwa, Min Xu, Niwa, Kenta, Xu, Min, Haimin Zhang, Zhang, Haimin, Zhang, Guoqiang, Zhang, H, Niwa, K   +11 more
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Simplifying Graph Convolutional Networks

CoRR, 2019
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess
Felix Wu, Tianyi Zhang 0007, Amauri H. Souza Jr., Christopher Fifty, Tao Yu, Kilian Q. Weinberger   +5 more
openaire   +3 more sources

Graph Convolutional Network Adversarial Attack Method for Brain Disease Diagnosis [PDF]

Jisuanji kexue, 2022
In recent years,brain functional networks analysis using the resting state functional magnetic resonance imaging data has been widely used in computer-aided diagnosis tasks of various brain diseases.The graph convolutional network framework integrating ...
WANG Xiao-ming, WEN Xu-yun, XU Meng-ting, ZHANG Dao-qiang
doaj   +1 more source

Graph Convolutional Neural Networks Sensitivity under Probabilistic Error Model [PDF]

, 2022
Graph Neural Networks (GNNs), particularly Graph Convolutional Neural Networks (GCNNs), have emerged as pivotal instruments in machine learning and signal processing for processing graph-structured data.
Wang, Xinjue, Ollila, Esa, Vorobyov, Sergiy A.   +2 more
core   +1 more source