graph neural networks

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

Abstract Background Ovarian cancer remains one of the most lethal gynecologic malignancies, largely due to pronounced molecular heterogeneity, nonspecific clinical presentation, and frequent diagnosis at advanced stages. Multiomics profiling—including genomics, transcriptomics, and epigenomics—offers a powerful avenue...

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...

Online intrusion detection in computer networks using edge-aware attentive graph neural network

Graph Neural Network (GNN)-based intrusion detection systems (IDS) have emerged as powerful tools for modeling the structural patterns of network traffic. However, most existing methods rely on large, temporally aggregated graphs and random train-test splits, which risk information leakage from future traffic and overs...

AlloPool is a deep learning framework that infers protein allostery from molecular dynamics simulations

Recent advances in artificial intelligence have transformed protein structure prediction and design. However, protein function is governed not only by static structures but also by the conformational dynamics that allow proteins to access distinct functional states. Predicting these dynamic transitions, central to many...