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LightCapsGNN: light capsule graph neural network for graph classification
Graph neural networks (GNNs) have achieved excellent performances in many graph-related tasks. However, they need appropriate pooling operations to...
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A faster deep graph clustering network based on dynamic graph weight update mechanism
Deep graph clustering has attracted considerable attention for its potential in handling complex graph-structured data. However, existing approaches...
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Consensus Affinity Graph Learning via Structure Graph Fusion and Block Diagonal Representation for Multiview Clustering
Learning a robust affinity graph is fundamental to graph-based clustering methods. However, some existing affinity graph learning methods have...
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MDGCL: Graph Contrastive Learning Framework with Multiple Graph Diffusion Methods
In recent years, some classical graph contrastive learning(GCL) frameworks have been proposed to address the problem of sparse labeling of graph data...
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Adaptive graph contrastive learning with joint optimization of data augmentation and graph encoder
Graph contrastive learning (GCL) has been successfully used to solve the problem of the huge cost of graph data annotation, such as labor cost, time...
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Attentive graph structure learning embedded in deep spatial-temporal graph neural network for traffic forecasting
AbstractA smooth traffic flow is very crucial for an intelligent traffic system. Consequently, traffic forecasting is critical in achieving...
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Intra-graph and Inter-graph joint information propagation network with third-order text graph tensor for fake news detection
Although the Internet and social media provide people with a range of opportunities and benefits in a variety of ways, the proliferation of fake news...
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Directed dynamic attribute graph anomaly detection based on evolved graph attention for blockchain
Blockchain is gradually becoming an important data storage platform for Internet digital copyright confirmation, electronic deposit, and data...
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Graph-Segmenter: graph transformer with boundary-aware attention for semantic segmentation
The transformer-based semantic segmentation approaches, which divide the image into different regions by sliding windows and model the relation...
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Multi-view graph representation learning for hyperspectral image classification with spectral–spatial graph neural networks
Hyperspectral image (HSI) classification benefits from effectively handling both spectral and spatial features. However, deep learning (DL) models,...
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Learning to solve graph metric dimension problem based on graph contrastive learning
Deep learning has been widely used to solve graph and combinatorial optimization problems. However, proper model deployment is critical for training...
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DC-Graph: a chunk optimization model based on document classification and graph learning
Existing machine reading comprehension methods use a fixed stride to chunk long texts, which leads to missing contextual information at the...
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Long-tailed graph neural networks via graph structure learning for node classification
Long-tailed methods have gained increasing attention and achieved excellent performance due to the long-tailed distribution in graphs, i.e., many...
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Incorporating self-attentions into robust spatial-temporal graph representation learning against dynamic graph perturbations
This paper proposes a Robust Spatial-Temporal Graph Neural Network (RSTGNN), which overcomes the limitations faced by graph-based models against...
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Graph construction on complex spatiotemporal data for enhancing graph neural network-based approaches
Graph neural networks (GNNs) haven proven to be an indispensable approach in modeling complex data, in particular spatial temporal data, e.g.,...
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SimGNN: simplified graph neural networks for session-based recommendation
Session-based recommender systems (SBR) aim to predict the next action of an anonymous user session. Recently Graph Neural Networks (GNN) models have...
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Intensifying graph diffusion-based salient object detection with sparse graph weighting
Salient object detection based on the diffusion process on the graph has achieved considerable performance. It mainly depends on the affinity matrix...
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Multi-Head Attention and Knowledge Graph Based Dual Target Graph Collaborative Filtering Network
Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein,...
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Multi-view parallel graph pooling
Graph pooling is a crucial operation in graph neural networks (GNNs) for down-sampling. It is noted that existing methods for graph pooling suffer...
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A cross-linguistic entity alignment method based on graph convolutional neural network and graph attention network
Cross-language entity alignment forms an important component of building a Knowledge Graph. The task of cross-lingual entity alignment is to match...
