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Exploiting Edge Features in Graph Neural Networks
[article]
2019
arXiv
pre-print
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Third, for the proposed new framework, edge features are adaptive across network layers. As a result, our proposed new framework and new models can exploit a rich source of graph information. ...
approches used in current graph neural networks. ...
Then, based on the proposed new attention mechanism, we propose a new graph neural network architecture that adapts edge features across neural network layers. ...
arXiv:1809.02709v2
fatcat:k64r3grrqvaknbmulbfti6e7w4
Customized Graph Neural Networks
[article]
2021
arXiv
pre-print
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Recently, Graph Neural Networks (GNNs) have greatly advanced the task of graph classification. ...
Therefore, in this paper, we aim to develop customized graph neural networks for graph classification. ...
Recently, graph neural networks (GNNs) have generalized deep neural networks to graph data. ...
arXiv:2005.12386v2
fatcat:5rk5nsdodrgzvmq37nx2xe47je
Multi-Hops Functional Connectivity Improves Individual Prediction of Fusiform Face Activation via a Graph Neural Network
2021
Frontiers in Neuroscience
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To test this proposal, we used multi-hops functional connectivity to predict the individual face activation of the right fusiform face area (rFFA) via a multi-layer graph neural network and showed that ...
Results also indicated that the two-layer graph neural network is the best in characterizing rFFA's face activation and revealed a hierarchical network for the face processing of rFFA. ...
of the multi-layer graph neural network. ...
doi:10.3389/fnins.2020.596109
pmid:33519356
pmcid:PMC7840579
fatcat:3mu65ovgirbihbapxqayo7h3yq
Graph Attention Network based Pruning for Reconstructing 3D Liver Vessel Morphology from Contrasted CT Images
[article]
2020
arXiv
pre-print
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In this paper, we propose a framework for liver vessel morphology reconstruction using both a fully convolutional neural network and a graph attention network. ...
With the injection of contrast material into blood vessels, multi-phase contrasted CT images can enhance the visibility of vessel networks in the human body. ...
To better model the vessel connectivity, the graph neural network (GNN) has been adapted into the image segmentation model [16] . ...
arXiv:2003.07999v1
fatcat:ucw2thsl5vgqhk5zowghc4wvwm
Capsule Graph Neural Network for Multi-Label Image Recognition (Student Abstract)
2022
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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graph convolutional network module that leverages the popular graph convolutional networks with an adaptive label correlation graph to model label dependencies. ...
This paper studies the problem of learning complex relationships between multi-labels for image recognition. Its challenges come from the rich and diverse semantic information in images. ...
Methodology In this work, we present a capsule graph neural network for multi-label image recognition named CGML to address the above challenges. ...
doi:10.1609/aaai.v36i11.21695
fatcat:3czdtxmdgjev7pxhj27yx2pbda
Image Clustering with Contrastive Learning and Multi-scale Graph Convolutional Networks
[article]
2022
arXiv
pre-print
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convolutional neural network (CNN) and graph convolutional network (GCN) as well as the gap between contrastive learning and multi-scale neighborhood structure learning for the image clustering task. ...
In view of this, this paper presents a new deep clustering approach termed Image clustering with contrastive learning and multi-scale Graph Convolutional Networks (IcicleGCN), which bridges the gap between ...
Graph Convolutional Network The concept of graph neural network (GNN) was first proposed by Gori et al. [24] and further elaborated in GNN* [25] . ...
arXiv:2207.07173v1
fatcat:jrr3mxg525ekvnluq32dl2uo3q
Spatiotemporal Graph Convolutional Network for Multi-Scale Traffic Forecasting
2022
ISPRS International Journal of Geo-Information
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To address this multi-scale problem, we adopted the idea of Res2Net and designed a hierarchical temporal attention layer and hierarchical adaptive graph convolution layer. ...
The spatiotemporal graph neural network has attracted attention from academic and business domains for its powerful spatiotemporal pattern capturing capability. ...
As for the hierarchical attention layer and the hierarchical adaptive graph convolution layer, they implement the capture of multi-scale temporal and multi-scale spatial information on a layer-by-layer ...
doi:10.3390/ijgi11020102
fatcat:vfbjdc7t3bfyre3cbu5df3m4mm
Adaptive backstepping synchronization for networked Lagrangian systems
2012
International Journal of Computer Applications
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Compared with all the cited references that deal with the standard adaptive 2 j=1 synchronization [28], [3] the proposed control law uses the adaptive neural network that essentially avoids the computation ...
Then, in order to deal with the presence of parametric modeling uncertainties, the control law has been extended to adaptive controller based on neural network algorithm . ...
Neural Network background Consider the multilayer neural network architecture presented in F ig.3. ...
doi:10.5120/5741-7608
fatcat:zc5d6gkdtvbyjpalk3snekoizy
Enhancement of Shadow Region in an Image using Artificial Neural Network
2020
VOLUME-8 ISSUE-10, AUGUST 2019, REGULAR ISSUE
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Proposed system uses multi layer feed forward artificial neural network. Error Back Propagation algorithm is used in training process. Desired data is obtained using log transformation method. ...
Networks, Adaptive Interpolation Method, Contrast Stretching, Range Compression, Alpha Rooting and Spatially Adaptive Iterative Filtering and Multi-Frame Super Resolution [1] . ...
We have selected back propagation neural network training algorithm to implement a multi layered neural network.
Fig. 2.Error back-propagation training algorithm [2] B. ...
doi:10.35940/ijitee.c7942.019320
fatcat:ei2xa67wwbgphcuyr43hlzpcba
Contrastive Meta Learning with Behavior Multiplicity for Recommendation
[article]
2022
arXiv
pre-print
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In addition, to capture the diverse multi-behavior patterns, we design a contrastive meta network to encode the customized behavior heterogeneity for different users. ...
In particular, we propose a multi-behavior contrastive learning framework to distill transferable knowledge across different types of behaviors via the constructed contrastive loss. ...
In particular: i) In the first stage, we integrate the behavior-aware graph neural network (with cloned state) and contrastive meta network, to learn initial parameter space of our multi-behavior contrastive ...
arXiv:2202.08523v1
fatcat:mvx3u5hxkvh2hekxisptmupyp4
ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network
[article]
2021
arXiv
pre-print
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Graph Neural Networks (GNNs) have been widely studied in various graph data mining tasks. ...
Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning. ...
For neural network methods, we consider Multi-Layer Perceptron (MLP) in Euclidean space and HNN [16] in hyperbolic space. ...
arXiv:2110.07888v1
fatcat:2tqj2vqd2rgjbizrukiidzxbou
Instance-Adaptive Graph for EEG Emotion Recognition
2020
PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE
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To give a more precise graphic representation, we design the multi-level and multi-graph convolutional operation and the graph coarsening. ...
To tackle the individual differences and characterize the dynamic relationships among different EEG regions for EEG emotion recognition, in this paper, we propose a novel instance-adaptive graph method ...
EEG signals are processed by multi-level and multi-graph convolution, graph coarsening, region dependency modeling, full connection layer (FC) and softmax layer. ...
doi:10.1609/aaai.v34i03.5656
fatcat:xgvqsaroh5fmzjn46ma3asmwda
Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks
[article]
2021
arXiv
pre-print
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In this paper, we propose RioGNN, a novel Reinforced, recursive and flexible neighborhood selection guided multi-relational Graph Neural Network architecture, to navigate complexity of neural network structures ...
Graph Neural Networks (GNNs) have been widely used for the representation learning of various structured graph data. ...
graph neural network model used to learn the adaptive receptive domain of neural networks defined on permutation invariant graph data. ...
arXiv:2104.07886v2
fatcat:5loufv5m6bcp5ny2rzd7aqnuju
Temporal Contrastive Graph Learning for Video Action Recognition and Retrieval
[article]
2021
arXiv
pre-print
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To model multi-scale temporal dependencies, our TCGL integrates the prior knowledge about the frame and snippet orders into graph structures, i.e., the intra-/inter- snippet temporal contrastive graphs ...
In contrast to the existing methods that ignore modeling elaborate temporal dependencies, our TCGL roots in a hybrid graph contrastive learning strategy to jointly regard the inter-snippet and intra-snippet ...
multi-layer perception. ...
arXiv:2101.00820v8
fatcat:377k53ms55gcfgxzl533xqt5vi
A Survey of Multi-Focus Image Fusion Methods
2022
Applied Sciences
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At the end of this paper, some main limitations in current research are discussed, and the future development of multi-focus image fusion is prospected. ...
As an important branch in the field of image fusion, the multi-focus image fusion technique can effectively solve the problem of optical lens depth of field, making two or more partially focused images ...
This method proved that the decision graph obtained by a multi-scale convolutional neural network is reliable and can produce high-quality fusion images. Ma et al. ...
doi:10.3390/app12126281
fatcat:mzzmnt63ofcovg7w2ya6rdfoye
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