12 results match your criteria edges gnn

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Enhancing Graph Neural Networks by a High-quality Aggregation of Beneficial Information.

Neural Netw 2021 Apr 24;142:20-33. Epub 2021 Apr 24.

School of Computer Science, National Engineering Research Center for Multimedia Software and Institute of Artificial Intelligence, Wuhan University, PR China; Shenzhen Research Institute, Wuhan University, PR China. Electronic address:

Graph Neural Networks (GNNs), such as GCN, GraphSAGE, GAT, and SGC, have achieved state-of-the-art performance on a wide range of graph-based tasks. These models all use a technique called neighborhood aggregation, in which the embedding of each node is updated by aggregating the embeddings of its neighbors. However, not all information aggregated from neighbors is beneficial. Read More

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Damage Localization and Severity Assessment of a Cable-Stayed Bridge Using a Message Passing Neural Network.

Sensors (Basel) 2021 Apr 30;21(9). Epub 2021 Apr 30.

Civil and Environmental Engineering, Sejong University, Seoul 05006, Korea.

Cable-stayed bridges are damaged by multiple factors such as natural disasters, weather, and vehicle load. In particular, if the stayed cable, which is an essential and weak component of the cable-stayed bridge, is damaged, it may adversely affect the adjacent cables and worsen the bridge structure condition. Therefore, we must accurately determine the condition of the cable with a technology-based evaluation strategy. Read More

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Predicting the Dynamics of the COVID-19 Pandemic in the United States Using Graph Theory-Based Neural Networks.

Int J Environ Res Public Health 2021 04 6;18(7). Epub 2021 Apr 6.

Department of Sports Sciences, MATIM, Université de Reims Champagne-Ardenne, 51100 Reims, France.

The COVID-19 pandemic has had unprecedented social and economic consequences in the United States. Therefore, accurately predicting the dynamics of the pandemic can be very beneficial. Two main elements required for developing reliable predictions include: (1) a predictive model and (2) an indicator of the current condition and status of the pandemic. Read More

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Ricci Curvature-Based Semi-Supervised Learning on an Attributed Network.

Entropy (Basel) 2021 Feb 27;23(3). Epub 2021 Feb 27.

School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

In recent years, on the basis of drawing lessons from traditional neural network models, people have been paying more and more attention to the design of neural network architectures for processing graph structure data, which are called graph neural networks (GNN). GCN, namely, graph convolution networks, are neural network models in GNN. GCN extends the convolution operation from traditional data (such as images) to graph data, and it is essentially a feature extractor, which aggregates the features of neighborhood nodes into those of target nodes. Read More

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February 2021

On Inductive-Transductive Learning with Graph Neural Networks.

IEEE Trans Pattern Anal Mach Intell 2021 Jan 25;PP. Epub 2021 Jan 25.

Many realworld domains involve information naturally represented by graphs, where nodes denote basic patterns while edges stand for relationships among them. The Graph Neural Network (GNN) is a machine learning model capable of directly managing graphstructured data. In the original framework, GNNs are inductively trained, adapting their parameters based on a supervised learning environment. Read More

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January 2021

Hierarchical Representation Learning in Graph Neural Networks With Node Decimation Pooling.

IEEE Trans Neural Netw Learn Syst 2021 Feb 4;PP. Epub 2021 Feb 4.

In graph neural networks (GNNs), pooling operators compute local summaries of input graphs to capture their global properties, and they are fundamental for building deep GNNs that learn hierarchical representations. In this work, we propose the Node Decimation Pooling (NDP), a pooling operator for GNNs that generates coarser graphs while preserving the overall graph topology. During training, the GNN learns new node representations and fits them to a pyramid of coarsened graphs, which is computed offline in a preprocessing stage. Read More

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February 2021

Graph Neural Network and Context-Aware Based User Behavior Prediction and Recommendation System Research.

Comput Intell Neurosci 2020 30;2020:8812370. Epub 2020 Nov 30.

School of Computer Science and Technology, Qilu University of Technology (Shandong Academy of Sciences), Jinan, Shandong 250353, China.

Due to the influence of context information on user behavior, context-aware recommendation system (CARS) has attracted extensive attention in recent years. The most advanced context-aware recommendation system maps the original multi-field features into a shared hidden space and then simply connects it to a deep neural network (DNN) or other specially designed networks. However, for different areas, the ability of modeling complex interactions in a sufficiently flexible and explicit way is limited by the simple unstructured combination of feature fields. Read More

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November 2020

A Survey of Network Embedding for Drug Analysis and Prediction.

Curr Protein Pept Sci 2020 07 2. Epub 2020 Jul 2.

State Key Laboratory for Conservation and Utilization of Subtropical Agro-bioresources, Guangxi University, Nanning. China.

Traditional network-based computational methods have shown good results in drug analysis and prediction. However, these methods are time consuming and lack universality, and it is difficult to exploit the auxiliary information of nodes and edges. Network embedding provides a promising way for alleviating the above problems by transforming network into a low-dimensional space while preserving network structure and auxiliary information. Read More

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GNNExplainer: Generating Explanations for Graph Neural Networks.

Adv Neural Inf Process Syst 2019 Dec;32:9240-9251

Department of Computer Science, Stanford University.

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs. GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models and explaining predictions made by GNNs remains unsolved. Read More

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December 2019

Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences.

Bioinformatics 2019 01;35(2):309-318

National Institute of Advanced Industrial Science and Technology, Artificial Intelligence Research Center, Tokyo, Japan.

Motivation: In bioinformatics, machine learning-based methods that predict the compound-protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symbolic data (e.g. Read More

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January 2019

AGeNNT: annotation of enzyme families by means of refined neighborhood networks.

BMC Bioinformatics 2017 May 25;18(1):274. Epub 2017 May 25.

Institute of Biophysics and Physical Biochemistry, University of Regensburg, D-93040, Regensburg, Germany.

Background: Large enzyme families may contain functionally diverse members that give rise to clusters in a sequence similarity network (SSN). In prokaryotes, the genome neighborhood of a gene-product is indicative of its function and thus, a genome neighborhood network (GNN) deduced for an SSN provides strong clues to the specific function of enzymes constituting the different clusters. The Enzyme Function Initiative ( http://enzymefunction. Read More

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Graphene nanonet for biological sensing applications.

Nanotechnology 2013 Sep 21;24(37):375302. Epub 2013 Aug 21.

Department of Physics and Astronomy, Seoul National University, Seoul, Korea.

We report a simple but efficient method to fabricate versatile graphene nanonet (GNN)-devices. In this method, networks of V2O5 nanowires (NWs) were prepared in specific regions of single-layer graphene, and the graphene layer was selectively etched via a reactive ion etching method using the V2O5 NWs as a shadow mask. The process allowed us to prepare large scale patterns of GNN structures which were comprised of continuous networks of graphene nanoribbons (GNRs) with chemical functional groups on their edges. Read More

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September 2013
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