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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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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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Attribute prediction of spatio-temporal graph nodes based on weighted graph diffusion convolution network
Spatio-temporal graph data can be analyzed by effectively mining for realizing spatio-temporal graph data prediction. It is of great significance to...
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Tertiary Lymphoid Structures Generation Through Graph-Based Diffusion
Graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing...
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Addiction-related brain networks identification via Graph Diffusion Reconstruction Network
Functional magnetic resonance imaging (fMRI) provides insights into complex patterns of brain functional changes, making it a valuable tool for...
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HGIM: Influence maximization in diffusion cascades from the perspective of heterogeneous graph
When solving the problem of influence maximization (IM) in social networks accompanied by diffusion cascades, existing related methods face some...
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Graph-Based Diffusion Method for Top-N Recommendation
Data that may be used for personalised recommendation purposes can intuitively be modelled as a graph. Users can be linked to item data; item data...
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Estimating Diffusion Degree on Graph Stream Generated from Social and Web Networks
Data stream generated from different Web 2.0 applications may contains data which is best described by graphs. Graph streams thus generated show big...
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Secure chaotic image encryption method using random graph traversal and three step diffusion
In this paper, we have presented an image encryption technique using random graph traversal, chaotic maps, and a secure hashing algorithm. Due to the...
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Diffusion-Based Graph Super-Resolution with Application to Connectomics
The super-resolution of low-resolution brain graphs, also known as brain connectomes, is a crucial aspect of neuroimaging research, especially in...
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Dynamic Graph-Driven Heat Diffusion: Enhancing Industrial Semantic Segmentation
Dust significantly impacts construction progress and worker health, necessitating the use of machine learning for dust area identification and...
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JointGraph: joint pre-training framework for traffic forecasting with spatial-temporal gating diffusion graph attention network
Accurate highway traffic forecasting is a critical task in intelligent transportation systems (ITSs), which needs to capture complex spatiotemporal...
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Adaptive Randomized Graph Neural Network Based on Markov Diffusion Kernel
Graph neural networks (GNNs) especially Graph convolutional networks (GCNs) GCNs, are popular in graph representation learning. However, GCNs only...
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Graph Diffusion Reconstruction Network for Addictive Brain-Networks Identification
Functional Magnetic Resonance Imaging(fMRI) can reveal complex patterns of brain functional changes. The exploration of addiction-related brain...
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Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting
The probabilistic estimation for multivariate time series forecasting has recently become a trend in various research fields, such as traffic,...
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A new framework for graph neural network with local information diffusion
Graph neural networks have attracted a lot of attention in recent years. In most graph neural networks, the representation of a node is obtained by...
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Attention-enabled adaptive Markov graph convolution
GNNs (Graph Neural Networks) have attracted increasing attention for their strong power on dealing with the graph structures. However, it remains a...
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Observation Is Reality? A Graph Diffusion-Based Approach for Service Tags Recommendation
Accurate service tags recommendation plays a crucial role in classifying, searching, managing, composing, and expanding services. However, many...
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Heterogeneous graph neural network with graph-data augmentation and adaptive denoising
Heterogeneous graphs are especially important in our daily life, which describe objects and their connections through nodes and edges. For this...
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A Diffusion Simulation User Behavior Perception Attention Network for Information Diffusion Prediction
Information diffusion prediction is an essential task in understanding the dissemination of information on social networks. Its objective is to...