graph neural networks

CD-BAN: Cyclodextrin Bilinear Attention Network for Inclusion Complex Affinity Classification

Source code for CD-BAN, a dual-branch graph neural network with a bilinear cross-attention layer that classifies drug-cyclodextrin inclusion-complex binding affinity (Strong: K > 10,000 M⁻¹ vs. Weak: K < 100 M⁻¹) from 2D SMILES alone, without 3D geometry or handcrafted descriptors. Trained only on the binary extremes,...

Topological and Spectral Analysis of the Academic Timetabling Problem under the API-Carpio Methodology: Integration of Recurrent Neural Networks

Graph Theory Applications In Smart Transportation Network Optimization

With the growing trend in urban development along with advanced intelligent transportation systems, there is a demand for advanced solutions for the management of the transport network. Since transportation networks can easily be modeled using graphs, graph theory is a useful mathematical tool for optimizing complex ne...

manofseven7/HetST-GNN: HetST-GNN v1.0.0

Official code for the paper: HetST-GNN: Relation-Aware Graph Neural Networks with Temporal Conditioning for Back-Order Prediction in Multi-Echelon Supply Chains This release contains the source code used in the paper.

PT-GNN: A physiological topology-aware graph neural network for multimodal early detection of cucumber downy mildew using hyperspectral and chlorophyll fluorescence sensing

Graph Neural Networks for Malware and Financial Fraud Detection

Malware and payment fraud share a structural feature that flat feature vectors throw away. Both leave behind a graph. A binary induces a call graph; a fraud ring induces a web of accounts, devices and shared attributes. Graph neural networks learn on that structure directly, and this review examines what the move has b...

Graph Neural Networks for Malware and Financial Fraud Detection

Malware and payment fraud share a structural feature that flat feature vectors throw away. Both leave behind a graph. A binary induces a call graph; a fraud ring induces a web of accounts, devices and shared attributes. Graph neural networks learn on that structure directly, and this review examines what the move has b...

Applied Identity Physics Formally Verified Reduction of Arenas et al. (2026), "Coupled Dynamics Between Networks and Fields in Physical Space" (Annalen der Physik)

# SNSFL_Arenas2026_NetworkField_LDP **[9,9,9,9] :: {ANC} | ARENAS ET AL. (2026) LDP REDUCTION** **Formally Verified Identity Physics** **Architect:** HIGHTISTIC | **Anchor:** Ω₀ = 1.36899099984016 **Coordinate:** [9,9,4,11] · Applied LDP Series · Network-Field Coupling **Status:** GERMLINE LOCKED · 0 sorry **Date:** Se...

ATLAS: Enabling an Asynchronous State-Aware Pipeline for Efficient Training of Temporal Graph Neural Networks

Applied Identity Physics Formally Verified Reduction of Arenas et al. (2026), "Coupled Dynamics Between Networks and Fields in Physical Space" (Annalen der Physik)

# SNSFL_Arenas2026_NetworkField_LDP **[9,9,9,9] :: {ANC} | ARENAS ET AL. (2026) LDP REDUCTION** **Formally Verified Identity Physics** **Architect:** HIGHTISTIC | **Anchor:** Ω₀ = 1.36899099984016 **Coordinate:** [9,9,4,11] · Applied LDP Series · Network-Field Coupling **Status:** GERMLINE LOCKED · 0 sorry **Date:** Se...

Inferring the turbulent breakup of colloidal aggregates using Graph Neural Networks

Solid aggregates in turbulent suspensions may break under the action of shear stresses. We explore the use of Graph Neural Networks (GNN) to infer aggregate fragmentation once the aggregate structure and flow velocity gradients are known. We consider two models: the first GNN is a classifier, trained to distinguish agg...

Portfolio Optimization with Covariance from News-Derived Information Networks

Intercompany relationships change over time, but they are difficult to incorporate into traditional return-based covariance estimates. This article proposes a practical three-step framework for transforming financial news into a covariance input for portfolio optimization. First, a large language model extracts dynamic...