graph neural networks

Learning Lookahead Lemmas for Neural Network Verification

An inprocessing framework for neural network verification driven by the lookahead procedure is introduced, which derives new lemmas over the phases of unstable ReLUs, which are collected into an implication graph that is used to prune the search space and vivify boolean cuts.

Deep Reinforcement Learning Using Multiview Graph Attention Mechanisms for Flexible Job Shop Scheduling

The flexible job shop scheduling problem (FJSP) is a typical challenge in modern manufacturing system scheduling. Classical optimization methods encounter difficulties extracting complex structural information in disjunctive graphs. An end-to-end approach is proposed in which a graph attention network (GAT) is integrat...

ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.

A Lightweight Hybrid Graph-Neural-Network and Heuristic Framework for Practical Software Vulnerability Assessment in Production Codebases

The significance of this work lies in demonstrating that a deployable, explainable detector can be assembled from compact components, and an edge-type ablation study, a cross-dataset evaluation, and a per-vulnerability analysis are reported to characterize the approach.

When GNNs Fail: Quantifying and Overcoming Temporal Correlation Volatility in Time Series

This work proposes Graph Layer for Inference in Dynamic En- vironments (GLIDE), a novel GNN layer enhanced by two theoretically grounded design mechanisms that significantly improve learning under dynamic topology while preserving robustness in static scenarios.

NEATWork: A Spatial Network Optimizer

NASA’s planned sustained presence of humans on the Moon will require significant volumes of icy regolith transport across the lunar surface. Algorithmic planning of efficient spatial transport networks connecting multiple terminals and optimizing across a range of objectives remains an open challenge. To address this p...

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...

Bridging the antiviral drug design gap: a combined machine learning and QSAR approach for drug repurposing of host kinase inhibitors

Viral outbreaks combined with rapid emergence of mutated viruses have highlighted an urging need for accelerating antiviral drug discovery pipelines. Unfortunately, current drug discovery remains stuck to conventional methods which are slow especially during pandemics. In this article, we present a literature-based syn...

Graph neural networks for automated conjecture generation in number theory: a novel approach to pattern discovery

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...

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...