Learning adaptive neighborhoods for graph neural networks

Learning adaptive neighborhoods for graph neural networks

Graph convolutional networks (GCNs) enable end-to-end learning on graph structured data. However, many works assume a given graph structure. When the input graph is noisy or unavailable, one approach is to construct or learn a latent graph structure. These methods typically fix the choice of node de...

Disgrifiad llawn

Manylion Llyfryddiaeth
Prif Awduron:	Saha, A, Mendez, O, Russell, C, Bowden, R
Fformat:	Conference item
Iaith:	English
Cyhoeddwyd:	IEEE 2024

Eitemau Tebyg

Graph Convolutional Network with Adaptive Fusion of Neighborhood Aggregation and Interaction
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AFGN: Adaptive Filtering Graph Neural Network for Few-Shot Learning
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Cyhoeddwyd: (2024-05-01)

Minimum Neighborhood Domination of Split Graph of Graphs
gan: ANJALINE. W, et al.
Cyhoeddwyd: (2023-03-01)

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Cyhoeddwyd: (2024-05-01)

The Neighborhood Polynomial of Chordal Graphs
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Cyhoeddwyd: (2022-05-01)

Graph Operations and Neighborhood Polynomials
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Cyhoeddwyd: (2021-08-01)

Graph Exponentiation and Neighborhood Reconstruction
gan: Hammack Richard H.
Cyhoeddwyd: (2021-02-01)

Multiscale Global Adaptive Attention Graph Neural Network
gan: GOU Ruru, YANG Wenzhu, LUO Zifei, YUAN Yunfeng
Cyhoeddwyd: (2023-12-01)

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gan: Yan, Yige
Cyhoeddwyd: (2023)

An adaptive multi-graph neural network with multimodal feature fusion learning for MDD detection
gan: Tao Xing, et al.
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Deep learning with knowledge graphs using graph neural networks
gan: Liu, S
Cyhoeddwyd: (2024)

Graph Neural Network Learning on the Pediatric Structural Connectome
gan: Anand Srinivasan, et al.
Cyhoeddwyd: (2025-01-01)

Graphs of Neighborhood Metric Dimension Two
gan: Badekara Sooryanarayana, et al.
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On open neighborhood locating-dominating in graphs
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Cyhoeddwyd: (2014-10-01)

Graph convolutional network for fMRI analysis based on connectivity neighborhood
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Graph Convolutional Network Using Adaptive Neighborhood Laplacian Matrix for Hyperspectral Images with Application to Rice Seed Image Classification
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On classes of neighborhood resolving sets of a graph
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Cyhoeddwyd: (2023)

Contrastive learning for heterogeneous graph neural networks
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GRAPH NEURAL NETWORK LEARNING IN LARGE GRAPHS - A CRITICAL REVIEW
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An End-to-End Multiplex Graph Neural Network for Graph Representation Learning
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Cyhoeddwyd: (2021-01-01)

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Graph neural network with knowledge graph
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A Principal Neighborhood Aggregation-Based Graph Convolutional Network for Pneumonia Detection
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Novel Graph Neighborhoods Emerging from Ideals
gan: Ayşegül Çaksu Güler, et al.
Cyhoeddwyd: (2023-05-01)

Graphs with Unique Maximum Packing of Closed Neighborhoods
gan: Božović Dragana, et al.
Cyhoeddwyd: (2022-08-01)

Polymer graph neural networks for multitask property learning
gan: Owen Queen, et al.
Cyhoeddwyd: (2023-05-01)

The common minimal common neighborhood dominating signed graphs
gan: Kavita S Permi, et al.
Cyhoeddwyd: (2013-03-01)

Learning Weight Signed Network Embedding with Graph Neural Networks
gan: Zekun Lu, et al.
Cyhoeddwyd: (2023-02-01)

Graph Neural Networks for Mesh Generation and Adaptation in Structural and Fluid Mechanics
gan: Ugo Pelissier, et al.
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A Block-Based Adaptive Decoupling Framework for Graph Neural Networks
gan: Xu Shen, et al.
Cyhoeddwyd: (2022-08-01)

Matrix Completion of Adaptive Jumping Graph Neural Networks for Recommendation Systems
gan: Xiaodong Zhu, et al.
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Adaptive competitive learning neural networks
gan: Ahmed R. Abas
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Graph contrast learning for recommendation based on relational graph convolutional neural network
gan: Xiaoyang Liu, et al.
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