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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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Virtual node graph neural network for full phonon prediction
Understanding the structure–property relationship is crucial for designing materials with desired properties. The past few years have witnessed...
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Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders
Inspired by recent progress in text-conditioned image generation, we propose a model for the problem of text-conditioned graph generation. We...
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TransG-net: transformer and graph neural network based multi-modal data fusion network for molecular properties prediction
Molecular properties prediction is an important task in the field of materials, especially in computational drug and materials discovery. Deep...
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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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Interpreting NMR Spectra by Constraint Solving
Nuclear Magnetic Resonance (NMR) spectroscopy is a widely used analytical technique for identifying the molecular structure of complex organic...
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Strongly regular graphs decomposable into a divisible design graph and a Hoffman coclique
In 2022, the second author found a prolific construction of strongly regular graphs, which is based on joining a coclique and a divisible design...
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A deep graph kernel-based time series classification algorithm
Time series data are sequences of values that are obtained by sampling a signal at a fixed frequency, and time series classification algorithms...
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Elucidation of Molecular Substructures from Nuclear Magnetic Resonance Spectra Using Gradient Boosting
Elucidating molecular structures from nuclear magnetic resonance (NMR) spectra poses a complex problem in the field of cheminformatics, namely the...
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Multi-dimensional spectral graph wavelet transform
In this paper, we introduce the two-dimensional spectral graph wavelet transform (SGWT) of discrete functions defined on weighted Cartesian product...
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Graph Neural Networks: Graph Transformation
Many problems regarding structured predictions are encountered in the process of “transforming” a graph in the source domain into another graph in...
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Expressive Graph Informer Networks
Applying machine learning to molecules is challenging because of their natural representation as graphs rather than vectors. Several architectures...
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Graph-Based LSTM for Anti-money Laundering: Experimenting Temporal Graph Convolutional Network with Bitcoin Data
Elliptic data—one of the largest Bitcoin transaction graphs—has admitted promising results in many studies using classical supervised learning and...
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The SPECTRA Project: Biomedical Data for Supporting the Detection of Treatment Resistant Schizophrenia
The SPECTRA project aims at supporting the clinician in the detection of patients suffering from a specific subclass of Schizophrenia (SZ),...
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DNGAE: Deep Neighborhood Graph Autoencoder for Robust Blind Hyperspectral Unmixing
Recently, Deep Learning (DL)-based unmixing techniques have gained popularity owing to the robust learning of Deep Neural Networks (DNNs). In...
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Global Attention-Based Graph Neural Networks for Node Classification
Graph attention networks (GATs) is an important method for processing graph data. The traditional GAT method can extract features from neighboring...
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Cyclic Group Spectra for Some Small Relation Algebras
The question of characterizing the (finite) representable relation algebras in a “nice" way is open. The class...
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Entropy-based kernel graph cut for textural image region segmentation
Recently, image segmentation based on graph cut methods has shown impressive performance on a set of image data. Although kernel graph cut provides...
