graph neural networks

Harnessing Trust in Directed Graphs: Redefining Robustness of Graph Learning

Existing research on robust Graph Neural Networks (GNNs) focuses predominantly on undirected graphs, overlooking the trustworthiness inherent in the link directions of directed graphs. In this work, we analyze the limitations of current approaches from both the attack and defense perspectives and redefine the robustnes...

Next-Generation Deep Learning: A Comprehensive Survey on Explainable, Efficient, Privacy-Preserving and Multimodal Artificial Intelligence

The continuous evolution of deep learning has significantly expanded the capabilities of artificial intelligence, enabling intelligent systems to solve increasingly complex problems across healthcare, computer vision, natural language processing, cybersecurity, finance, autonomous systems, and smart environments.Beginn...

XGNN-ID: Explainable Graph Neural Network Framework for Imbalanced Datasets

FOIN: A Federated Multi-Organoid Intelligence Architecture using Combinatorial Graph Modelling

The Graph Fourier Transform Recasts Rouse Modes to Encode Polymer Sequence

Rouse modes diagonalize the polymer connectivity matrix to describe how conformational motion is distributed over structural modes, yet the chemistry distributed along that same chain is traditionally encoded through hand-crafted, system-specific descriptors. Here, we generalize Rouse modes from bead coordinates to bea...

Graph neural network-based aquaculture net modeling and inference — A frequency domain approach

Advanced Mathematical Models, Biomathematics and Spatial Bioinformatics for the Discovery of Novel Therapeutic Targets

Rational therapeutic target discovery and systems biology face a decisive epistemological challenge: the fragmentation between the kinetic dynamics of intracellular pathways and the spatial-geometric complexity of macromolecular interactions. This work outlines an integrated analytical architecture that overcomes this...

BiGCNG: Bi-Path Graph Convolutional Neural Network with Gate Fusion for Person–Job Fit

Person–Job Fit (PJF) serves as a core task of intelligent recruitment recommendation. However, existing graph-based PJF models rely on a fixed, single-path aggregation scheme, thereby failing to simultaneously capture global interaction statistics and local competency-matching signals from candidate–job bipartite graph...

Adaptive Temporal Graph Neural Networks for Real-Time Financial Fraud Detection under Concept Drift

Financial fraud detectors operate in a non-stationary environment in which illicit actors adapt, class prevalence changes, and labels arrive after investigation. This paper proposes ATGNN-SP, a drift-gated stable-plastic temporal graph neural network for real-time-compatible fraud scoring. Each directed transaction sna...

MPGSD-AEB

This software package contains the Model04 EIGT-Net implementation developed for the MPGSD-AEB study. The package includes the trained model parameters, PyTorch model implementation, graph definition, MATLAB/Simulink forward-inference model, and fixed-step data feeder for hardware-in-the-loop preparation. The model pro...

A DeepMD-GNN Potential Reveals the Initiation Mechanism of a Trinitromethyl-Based Green Primary Explosive

Trinitromethyl-based green explosives have garnered extensive research interest by virtue of their high mass density, superior energy density, favorable oxygen balance, and eco-friendly properties. However, the development of new energetic materials remains challenging because experimental studies are costly, hazardous...

HJXiangLab/EdenGNN: v0.1.2

EdenGNN (Equivariant Density Graph Neural Network) is an open framework for accurate and efficient charge density prediction. Integrated with DFT softwares, it can predict electronic structures directly from atomic configurations.