graph neural networks

zeshanalvi/HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks: HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks

Initial version of the code for HAST-GNN-Multi-Task-Crime-Prediction-using-Spatio-Temporal-Graph-Neural-Networks.

Sparsity-aware social recommendation via information graph bottleneck

With the widespread adoption of online social networks, social recommender systems have become a key paradigm for mitigating data sparsity and cold-start issues in traditional collaborative filtering. However, existing graph neural network (GNN) based recommendation models face two fundamental structural bottlenecks wh...

Diffusion Models Beyond Computer Vision: Tabular Data Synthesis, Graph Generation, and Time-Series Forecasting

Abstract—Denoising Diffusion Probabilistic Models (DDPMs) and Score-Based Generative Models have fundamentally transformed continuous image generation and multi-modal synthesis. However, their mathematical extension beyond Euclidean continuous pixel grids into structured enterprise domains—specifically heterogeneous ta...

FusionEP: Adaptive Cross-Modal Deep Fusion Network for Essential Protein Identification via Multi-Omics Integration and Contrastive Learning

Identifying essential proteins lays a foundation for dissecting disease mechanisms and mining therapeutic targets. Existing algorithms mostly adopt unimodal inputs and struggle to capture multi-level biological interactions from genomics, subcellular localization and proteomics. We present FusionEP, which is an adaptiv...

Beyond pairwise interactions: Accurate forecasting of co-evolutionary spreading on higher-order networks

Many co-evolutionary spreading processes involve higher-order interactions, yet the consequences of approximating them through pairwise representations remain insufficiently understood. Here, we uncover distinctive prediction failures of pairwise graph neural networks during node-level one-step conditional transition-p...

BRAID: Learning Equilibrium Maps in Interdependent Security Games via Weight-Tied Iterative Graph Neural Networks

Scalable quantum error mitigation with physically informed graph neural networks

SPHERE-MS: Hybrid Fragment–Vocabulary Modeling for Accurate and Efficient Tandem Mass Spectra Prediction

Abstract Identifying known compounds and elucidating unknown chemical structures from mass spectra remains a significant challenge due to the limited size of experimental reference libraries. Enhancing these libraries with predicted mass spectra through computational methods offers an opportunity to resolve unmatched s...

Rollout-Aware Graph Neural Network-Based Simulator for Hydraulic Fracture

Abstract Hydraulic fracturing is a key technology for the efficient development of unconventional oil and gas reservoirs, and numerical simulation is an effective approach for investigating hydraulic fracture propagation. However, conventional hydraulic fracture propagation models struggle to achieve a satisfactory bal...

Multi-Modal Data-Driven Fracture Response Surrogate: Integrating Development History and Prior Knowledge for Efficient CO2 Storage Assessment

Abstract Depleted deep carbonate reservoirs with natural fracture networks are attractive targets for CO2 storage, yet they pose leakage risks from fracture reactivation. Existing approaches use numerical simulation and neural networks in isolation, sacrificing either interpretability or efficiency. To bridge this gap,...

A High-Fidelity Digital Twin Framework for Proppant Transport: Integrating GPU-Accelerated Barracuda Virtual Reactor MP-PIC with Graph Neural Networks

Abstract Optimizing proppant distribution across multiple clusters is essential for maximizing reservoir contact, yet Heel-to-Toe bias and limited entry inefficiencies remain pervasive. While high-fidelity Computational Fluid Dynamics (CFD) is an industry standard for visualizing these unseen downhole dynamics, a bridg...

Neuro-Symbolic Artificial Intelligence: Integrating Large Language Models with SMT Solvers, Knowledge Graphs, and Formal Automated Theorem Provers

Abstract—Modern deep neural networks, particularly autoregressive Large Language Models (LLMs), have achieved state-of-the-art empirical performance across natural language generation, semantic translation, and broad pattern recognition. However, pure statistical connectionist architectures suffer from fundamental epis...