graph neural networks

Detecting communities in social networks with graph convolutional neural networks

A new, end-to-end framework that combines heterogeneous node embeddings as Metapath2Vec with a multi-layer Graph Convolutional Network (GCN) with multi-head attention and adaptive neighbourhood aggregation is introduced.

Power Bond graph-embedded causal flow-aware graph neural network for fault localization of electro-hydraulic systems

To achieve precise fault localization for electro-hydraulic systems of mechanical, a Causal Flow-aware Graph Neural Network (CFAGNN) is proposed, which integrates power bond graph topology with deep learning. A power bond graph model is constructed to characterize energy transfer mechanisms and clarify dynamic coupling...

Counterfactual Explanations for Graph Neural Networks in Patient Outcome Prediction

Certified Bounds on the Fractional Chromatic Number of Planar Graphs by Learned Subgraph Selection: code, instances and certificates

Code, instances, certificates and results for the paper "Certified Bounds on the Fractional Chromatic Number of Planar Graphs by Learned Subgraph Selection". The method computes certified lower bounds on the fractional chromatic number of planar graphs. A graph neural network ranks vertices, a small induced subgraph is...

Certified Bounds on the Fractional Chromatic Number of Planar Graphs by Learned Subgraph Selection: code, instances and certificates

Code, instances, certificates and results for the paper "Certified Bounds on the Fractional Chromatic Number of Planar Graphs by Learned Subgraph Selection". The method computes certified lower bounds on the fractional chromatic number of planar graphs. A graph neural network ranks vertices, a small induced subgraph is...

CONFLUENCE: Does Cross-Scale Graph Attention Help Watershed Vegetation Forecasting?

Deep Generative and Graph-Based Representation Learning for Multiomics Survival Stratification in Ovarian Cancer: Secondary Analysis

The proposed VAE-based framework identified 2 prognostically distinct patient subgroups with significantly different overall survival and demonstrate the potential of deep generative representation learning for multiomics-based survival stratification in ovarian cancer.

Shaping Graph Neural Networks with Dynamical Systems

This work presents a unified dynamical-systems perspective for shaping approaches that explicitly control and regulate the degree of propagation, conservation, and dissipation of information throughout the neural flow, and highlights how neural differential equations provide a coherent theoretical framework for designi...

Interpretable GNN–Residual Kriging for spatial prediction and investigation-priority zoning of soil mercury in a karst agricultural region

Introduction Soil mercury (Hg) in karst agricultural regions is commonly characterized by strong local heterogeneity, which limits the ability of conventional interpolation methods to delineate localized enrichment. Methods We developed an interpretable graph neural network–residual kriging (GNN-RK) framework for soil...

Evolution of Neural Networks from a Random Graph to an Expander While Preserving Nonnormality: The Role of Bipartiteness and Synaptic Plasticity

Data and models for leakage-controlled PFAS bioactivity prediction with chemical-space-aware routing

Code, decontaminated data, and model weights for the manuscript "Chemical-space-aware routing enhances PFAS toxicity prediction: large-scale pretraining, conditional fine-tuning, and leakage control". The archive contains the complete pipeline for training and evaluating multi-task graph neural networks (Chemprop D-MPN...

Transferable Graph Neural Network Surrogates for Molecular Dynamics Across Crystal Symmetries

We present a transferable graph neural network (GNN) surrogate framework for molecular dynamics (MD) that directly predicts atomic displacements and propagates atomistic configurations without explicit force evaluation or numerical time integration. The central objective of this work is to establish whether a common GN...