graph neural networks

Learning on Edges: A Narrative Review of Graph Neural Networks from Recursive Networks to Geometric Deep Learning

Graph neural networks---learning over relational, irregular structure by passing messages between nodes---generalized deep learning's grids to the graph: molecules, social networks, knowledge bases, and the web. This article presents a narrative review of that arc's canonical line: Sperduti and Starita's 1997 structure...

Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy

This review systematically elaborates how ML models are driving the transformation of XAS data analysis, and provides a comprehensive overview of the key challenges in data‐driven XAS, including the feature engineering of spectra and structures, strategies for solving the “spectra‐to‐structure” Inverse Problem, and Sim...

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...

THE EVOLUTIONARY E-SPORTS CIVILIZATION MODEL (EECM): A Grand Unified Interdisciplinary Theory of Digital-Cognitive Civilization, Cognitive Capital, Artificial Intelligence Augmentation, Neuroeconomic Transformation, Virtual Ontology, and Civilizational Evolution

This paper proposes the Evolutionary E-Sports Civilization Model (EECM), a grand interdisciplinary theoretical framework designed to explain the transformation of gaming, cognition, digital interaction, artificial intelligence, and virtual systems into a new civilizational paradigm. The theory argues that E-sports repr...

pinn-serving: Derivative-Aware Serving of Physics-Informed Neural Networks

A serving harness and benchmark suite for physics-informed neural networks (PINNs). Introduces derivative-graph baking, which differentiates a tanh MLP symbolically offline and emits the closed-form derivative recursion as a forward-only program, making derivative-valued queries compatible with standard inference backe...

Graph Neural Networks for Spatio-Temporal Intrusion Detection

Cross-Database Performance of Speech-Based Graph Neural Networks for Parkinson’s Disease Detection

Graph Neural Networks for Spatio-Temporal Intrusion Detection

Smartpath-Rpl: A Deep Reinforcement Learning-Based Optimized Routing Protocol For Mobile Low-Power And Lossy Networks

QGPINNs: A Physics-Informed Neural Network Framework for Nonlocal Differential Equations on Quantum Graphs

We propose QGPINNs, a physics-informed neural network framework developed in PyTorch for the numerical solution of nonlocal differential equations on quantum graphs. The framework is designed as a general computational implementation in which the solution on each edge of the graph is approximated by a neural network, w...

Node-wise Feature Encoding for Neural Performance Prediction

This work introduces FeatureFormer, a neural performance predictor that incorporates explicit node-wise encodings of FLOPs, parameter counts, and memory proxies within a gated graph attention architecture and presents NNEQ, a new large-scale energy consumption dataset that enables unified evaluation of latency and ener...

Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution

Experiments and comparisons show that SEMGNN achieves competitive or improved predictive performance while providing more faithful and compact label-conditioned explanations, compared to post-hoc methods.