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Graph entropies-graph energies indices for quantifying network structural irregularity
Quantifying similarities/dissimilarities among different graph models and studying the irregularity (heterogeneity) of graphs in graphs and complex...
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Structural analysis of SARS-CoV-2 Spike protein variants through graph embedding
Since December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has affected almost all countries. The unprecedented spreading of...
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SEGCN: Structural Enhancement Graph Clustering Network
Deep graph clustering, which reveals the intrinsic structure and underlying relationship of graph node data, has become a highly concerning and...
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Sentiment analysis of tweets using text and graph multi-views learning
With the surge of deep learning framework, various studies have attempted to address the challenges of sentiment analysis of tweets (data sparsity,...
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Splitting Structural and Semantic Knowledge in Graph Autoencoders for Graph Regression
This paper introduces ReGenGraph, a new method for graph regression that combines two well-known modules: an autoencoder and a graph autoencoder. The...
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Graph-based comparative analysis of learning to rank datasets
The relative success of learning to rank algorithms has raised the attention of the research community for develo** efficient and effective ranking...
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A Graph Contrastive Learning Model Based on Structural and Semantic View for HIN Recommendation
With the rapid growth of information in the Internet era, people are in great need of recommendation methods to filter information. At present,...
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A Structural-Clustering Based Active Learning for Graph Neural Networks
In active learning for graph-structured data, Graph Neural Networks (GNNs) have shown effectiveness. However, a common challenge in these...
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Towards Visuo-Structural Handwriting Evaluation Based on Graph Matching
Judging the quality of handwriting based on visuo-structural criteria is fundamental for teachers when accompanying children who are learning to...
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Fusing structural information with knowledge enhanced text representation for knowledge graph completion
Although knowledge graphs store a large number of facts in the form of triplets, they are still limited by incompleteness. Hence, Knowledge Graph...
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Graph Analysis for Scalability Analysis
Scaling a parallel program to modern supercomputers is challenging due to inter-process communication, Amdahl’s law, and resource contention....
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Graph analysis using a GPU-based parallel algorithm: quantum clustering
The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a density-based unsupervised...
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SCS: A Structural Similarity Measure for Graph Clustering Based on Cycles and Paths
With the continuous development of business intelligence and scientific exploration, graphs have been extensively applied to various fields. Graph...
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Improving Structural and Semantic Global Knowledge in Graph Contrastive Learning with Distillation
Graph contrastive learning has emerged as a pivotal task in the realm of graph representation learning, with the primary objective of maximizing...
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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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Toward Interpretable Graph Classification via Concept-Focused Structural Correspondence
Despite significant achievements in numerous real-world applications, the black-box nature hinders GNNs from being adopted in high-stake decision...
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Multi-head Graph Convolutional Network for Structural Connectome Classification
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired...
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Joint learning of structural and textual information on propagation network by graph attention networks for rumor detection
Due to the advantages in information dissemination, social media is growing rapidly among the public but has also become a medium for the spread of...
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Heterogeneous graph neural networks analysis: a survey of techniques, evaluations and applications
Graph Neural Networks (GNNs) have achieved excellent performance of graph representation learning and attracted plenty of attentions in recent years....
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Method for the Adaptive Neutralization of Structural Breaches in Cyber-Physical Systems Based on Graph Artificial Neural Networks
AbstractThis paper presents a model of threats in cyber-physical systems (CPSs) with examples of attacks and potential negative consequences for...
