graph neural networks

EEG Signal-based Emotion Classification Using Channel Importance in Dual-path Graph Neural Networks and Cheetah Optimization Method

Neuroeconometrics and Neuroconnectometrics: Mathematical Foundations, Causal Identification, and Topological Manifolds

Neuroeconometrics and Neuroconnectometrics: Mathematical Foundations, Causal Identification, and Topological Manifolds presents an axiomatic, measure-theoretic, and differential-topological foundation unifying decision neurobiology, high-dimensional econometrics, and structural connectomics. The monograph resolves the...

SAGE: Signal-Amplified Guided Embeddings for Vulnerability Detection

Software vulnerabilities are a primary threat to modern infrastructure. While static analysis and Graph Neural Networks have long served as the foundation for vulnerability detection, the emergence of Large Language Models (LLMs) has introduced a transformative paradigm driven by superior semantic reasoning and cross-e...

Decoding brain anatomy from neuronal neighborhoods with MYCEL

Extracellular recordings carry information about neuronal location, and pooling per-unit predictions across neighboring electrodes improves its readout, but whether this local pooling should be learned is untested. We present MYCEL (Message-passing Yields Cellular Embeddings of Location), a graph neural network whose n...

DeepGreenGO: a graph neural network-based deep learning model for plant-specific protein function prediction

Automated protein function prediction remains challenging in plants because experimentally supported annotations are limited, particularly for crop and non-model plant species. Integrating deep learning techniques in plant molecular biology can offer a transformative opportunity to innovate research and support sustain...

Graph-Aware Reinforcement Learning for Adaptive APT Threat Hunting in Dynamic Enterprise Networks

Advanced Persistent Threats (APTs) are particularly challenging for enterprise intrusion detection because they are time-evolving, distributed, and difficult to detect under changing network conditions. Conventional machine-learning-based intrusion detection systems (IDSs) often rely on node-level features and may be v...

Federated Learning and Generative AI for Resilient Supply Chain Network Optimization: A Data-Driven Approach

Monitoring Change in 3D (MonChain3D)

MonChain3D, short for Monitoring Change in 3D, is a Python-based software suite for the processing, analysis, and comparison of 3D mesh data and mesh-derived graphs. It provides an integrated workflow encompassing mesh preprocessing, annotation and labeling, orientation and registration, graph construction and analysis...

Benchmarking Spatio-Temporal Graph Neural Networks for Time Series Forecasting Across Heterogeneous Graph Domains

An energy-based differentiable finite element framework for elastic and path-dependent elastoplastic solids

This paper proposes a finite-element-integrated deep energy method (DEM), hereafter referred to as FE-integrated DEM, for linear elasticity and path-dependent J 2 /von Mises plasticity. The method targets a central difficulty in neural elastoplastic solvers: path-dependent internal variables must be evolved consistentl...

OpenFOAM reference solutions of the 3D lid-driven cavity and 3D natural-convection cavity benchmarks

Converged OpenFOAM reference flow fields of the two steady benchmarks of the manuscript "Label-Free Finite-Volume-Residual Training of Attention Graph Neural Networks for Coupled Thermo-Fluid Fields". LDC_3D_lid_driven_cavity_OpenFOAM_reference.h5 holds the 3D lid-driven cavity (48^3 uniform mesh) at Re = 100, 400, 100...

Simulation Study on Digital Divide Propagation Path of Elderly Medical Treatment Based on Graph Neural Network and Public Social Network Data

To address the self-reinforcing diffusion of the digital divide in elderly medical treatment and the difficulty of precise intervention, this study proposes a Propagation–Adoption Coupled Graph Neural Network (PAC-GNN) and a path-level interpretable simulation framework. Using three types of public social network data,...