graph neural networks

An expert routing-driven graph framework from tongue multi-task features to liver fibrosis prediction

Liver fibrosis is a global health concern where early detection is crucial for timely intervention. Traditional Chinese Medicine tongue diagnosis offers a promising non-invasive assessment, but suffers from inter-observer variability and subjective interpretation. Current artificial intelligence approaches focus on bin...

Hard Symplectic Graph Neural ODEs for Scalable Trajectory Reconstruction in Perturbed Relativistic N-Body Celestial Swarms: Scaling to One Million Bodies with Empirical NASA Ephemerides

tfbs-mcp: a Model Context Protocol server for agentic transcription factor binding site analysis

tfbs-mcp is a Model Context Protocol (MCP) server for transcription factor (TF) binding site analysis. It exposes the steps of a binding-site study as 33 tools that a large language model (LLM) client, such as Claude Code, calls directly. Those steps run from peak extraction and dataset preparation through model traini...

Topology-aware dynamic co-evolution for robust graph learning.

Graph Neural Networks (GNNs) have achieved remarkable success in learning from graph-structured data. However, recent studies have revealed their vulnerability to adversarial attacks, where imperceptible perturbations to the graph structure can easily mislead predictions. Existing defense methods predominantly rely on...

Point-voxel absorbing graph representation learning for event stream-based recognition

Sampled point- and voxel-based representations are commonly used to compress dense event streams for efficient graph-based processing, where points or voxels are treated as graph nodes and encoded using graph neural networks (GNNs). However, existing event-based GNNs suffer from two main limitations. (1) they typically...

Forget Gradient Surgery: A Characterization of Projected Gradient Descent for Graph Edge Unlearning

Preprint of the paper 'Forget Gradient Surgery: A Characterization of Projected Gradient Descent for Graph Edge Unlearning' (submitted to TMLR). Machine unlearning for graph neural networks is complicated by message passing: deleting an edge from the adjacency matrix does not erase what the parameters learned from it,...

Molecular Structure Design of Heat-Resistant Cycloolefin Polymer Based on Machine Learning

Abstract Traditional biaxially oriented polypropylene (BOPP) is widely used in capacitors of converter valves, but its high-temperature performance is gradually approaching its design limit. Cycloolefin copolymer/polymer (COC/COP) materials are explored as promising alternatives due to their high glass transition tempe...

ACCELERATING MATERIALS DISCOVERY: LEARNING A UNIVERSAL REPRESENTATION OF CHEMICAL PROCESSES FOR CROSS-DOMAIN PROPERTY PREDICTION

Abstract Experimental validation of chemical processes is slow and costly, limiting exploration in materials discovery. Machine learning can prioritize promising candidates, but existing data in patents and literature is heterogeneous and difficult to use: records combine free text, numeric conditions, molecular struct...

Hybrid Graph Neural Network Ensemble with Stacked Meta-Learning for Laboratory-Conditioned Chromatographic Retention-Time Prediction

Abstract Accurate prediction of chromatographic retention time (RT) is valuable for compound identification in untargeted metabolomics and forensic toxicology, yet validation must account for repeated molecular structures, chemical-space coverage, and heterogeneous laboratory sources. Here we present a hybrid ensemble...

Ethics-Aware Hybrid Graph–Neuro-Symbolic Framework for Adaptive Fraud Management in Large-Scale Financial Systems

Fraud management systems are becoming more and more based on data-driven artificial intelligence processes to identify anomalous and malicious behavior in massive digital ecosystems. Nevertheless, the current solutions are usually characterized by low flexibility, inability to be interpreted, and such ethical limitatio...

Machine Learning-Based Vulnerability Detection in AI-Generated Source Code: A Systematic Literature Review and Research Directions

The rapid adoption of Generative AI and Large Language Models (LLMs) has transformed software development, but AI-generated code may contain security vulnerabilities such as injection flaws, weak authentication, and unsafe API usage. Traditional rule-based and static-analysis techniques struggle with semantically diver...

Modeling of coating ingredients via group contribution methods based on machine learning

This thesis develops a data-driven framework for predicting polymer properties using machine learning and chemically informed molecular representations. Polymer performance arises from complex interactions between molecular structure and formulation composition, which makes property prediction challenging. It can be se...