Key papers

• Gauge-Invariant, Parameter-Insensitive Regularization for Potential Recovery from Flow on Directed Graphs (2026)
  Mohammad Forouhesh
  5.82
• Graph-Theoretic Neural Network Fragmentation with Covariant Direct Molecular Force Learning: Enabling Coupled-Cluster Accuracy AIMD for Fluxional Systems (2026)
  Xiao Zhu et al.
  4.95
• CARDIAG: A Dense Segment Classification Benchmark of Deep Learning Architectures for Coronary Angiography (2026)
  Dominik Bernard Lau et al.
  4.95
• Graph Neural Networks for Scalable and Transferable Node Centrality Approximation (2026)
  Samra Sana et al.
  4.33
• Disentangling Knowledge States with Ability and Proficiency Modeling for Knowledge Tracing (2026)
  Duantengchuan Li et al.
  4.33
• A Hybrid Mamba for Audio-Visual Navigation (2026)
  Yi Wang et al.
  4.33
• Improving Molecular Property Prediction in Small Language Models Using Graph-based Tools (2026)
  Konstantinos Bougiatiotis et al.
  4.33
• Continuously Evolving Deepfake Detection: An Architecture and Public-Benchmark Evaluation of a Dynamic Detection System (2026)
  Ken Jon Miyachi et al.
  4.33
• Implementations of Quantum and Classical Topology-Aligned Architectures for Molecular Property Prediction (2026)
  James T. Pegg et al.
  4.33
• MxGPS: Multiplex Graph Transformers for a Power Grid Foundation Model (2026)
  Charilaos Papaioannou et al.
  4.33
• Transforming Rank: How Architecture Navigates the Spectral Pathologies of Depth (2026)
  Katie Everett
  4.33
• Spectral Flow Certificates for Depth-Aware Long-Range Propagation in Graph Neural Networks (2026)
  Ranjan Veerabhadraswamy et al.
  4.33
• CARNet Cycle-Conditioned Core Aggregation and Redistribution for Multivariate Time Series Forecasting (2026)
  Awsaf Tausif Adib et al.
  4.33
• A Graph-Based Control Interface for Traffic Signals on Heterogeneous Road Networks (2026)
  Bertil Braun
  4.33
• Efficient Recommendations via Graph Coarsening and Label Propagation (2026)
  Alessandro Sbandi et al.
  4.33
• Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts (2026)
  Keith G. Mills et al.
  4.33
• Universality and Approximation Rates of Graph Neural Networks with Random Features (2026)
  Lukas Gonon et al.
  4.33
• Enhanced Graph Neural Networks using K-Hop Gaussian Diffusion (2026)
  Xuling Zhang et al.
  4.27
• TMR-GGNN: Credit Card Fraud Detection based on Time-Aware Multi-Relational Guided Graph Neural Network (2026)
  Rohit Tewari et al.
  4.27
• Ramanujan Graph Rewiring with Non Negative Resistance Curvature (2026)
  Hugo Attali et al.
  4.27
• Auto-Configured Explainable Graph Neural Networks for Multi-Site Pollution Prediction (2026)
  Abdelkader Dairi et al.
  4.27
• Dynamic Link Prediction with Temporally Enhanced Signed Graph Neural Networks (2026)
  Derek Regier et al.
  4.22
• FB-GAT: A graph neural networks (GNNs) approach to assessing facades' buildability (2026)
  Bolun Wang et al.
  4.15
• A knowledge extrapolation model for attack inference based on graph attention networks and relation mapping (2026)
  Weiwu Ren et al.
  4.15
• Learning from Complementary Ultrasound Representations for Liver Disease Classification (2026)
  Sabahattin Mert Daloglu et al.
  3.45
• FLINT: Fingerprinting Federated Learning Architectures from 5G PHY-Layer Side Channels (2026)
  Md Nahid Hasan Shuvo et al.
  3.45
• Graph Attention Network-Aided Message-Passing Neural Decoder for Polar Codes Based on Sparse-Pruned Tanner Graphs (2026)
  Dongrui Fu et al.
  1.89
• Adaptive Structural Similarity Guided GCN–GAT Framework for Robust Link Prediction (2026)
  Shambhu Kumar et al.
  1.89
• GAT-GCN: Drug Molecular Solubility Prediction Based on Graph Neural Networks (2026)
  Yiyue Hu
  1.89
• Generalizing GNNs with Tokenized Mixture of Experts (2026)
  Xiaoguang Guo et al.
  1.67
• MG-HGNN: A Heterogeneous GNN Framework for Indoor Wi-Fi Fingerprint-Based Localization (2025)
  Yibu Wang et al.
  1.50
• Learning Joint Embeddings of Function and Process Call Graphs for Malware Detection (2025)
  Kartikeya Aneja and Nagender Aneja and Murat Kantarcioglu
  1.44
• DURENDAL: Graph deep learning framework for temporal heterogeneous networks (2023)
  Manuel Dileo et al.
  0.22
• Convergence of backpropagation with momentum for network architectures with skip connections (2017)
  Chirag Agarwal et al.
  —
• Deriving Neural Architectures from Sequence and Graph Kernels (2017)
  Tao Lei et al.
  —
• Graph Attention Networks (2017)
  Petar Veli\v{c}kovi\'c et al.
  —
• EnergyNet: Energy-based Adaptive Structural Learning of Artificial Neural Network Architectures (2017)
  Gus Kristiansen et al.
  —
• Motif-based Convolutional Neural Network on Graphs (2017)
  Aravind Sankar et al.
  —
• MotifNet: a motif-based Graph Convolutional Network for directed graphs (2018)
  Federico Monti et al.
  —
• N-GCN: Multi-scale Graph Convolution for Semi-supervised Node Classification (2018)
  Sami Abu-El-Haija et al.
  —
• Improving Graph Convolutional Networks with Non-Parametric Activation Functions (2018)
  Simone Scardapane et al.
  —
• Attention on Attention: Architectures for Visual Question Answering (VQA) (2018)
  Jasdeep Singh et al.
  —
• Convolutional Neural Network Architectures for Signals Supported on Graphs (2018)
  Fernando Gama and Antonio G. Marques and Geert Leus and Alejandro Ribeiro
  —
• Fusion Graph Convolutional Networks (2018)
  Priyesh Vijayan et al.
  —
• Data-driven Analytics for Business Architectures: Proposed Use of Graph Theory (2018)
  Lei Huang et al.
  —
• Hierarchical Graph Representation Learning with Differentiable Pooling (2018)
  Rex Ying et al.
  —
• Deep Graph Infomax (2018)
  Petar Veli\v{c}kovi\'c et al.
  —
• How Powerful are Graph Neural Networks? (2018)
  Keyulu Xu et al.
  —
• Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks (2018)
  Christopher Morris et al.
  —
• Multi-Task Graph Autoencoders (2018)
  Phi Vu Tran
  —
• Pitfalls of Graph Neural Network Evaluation (2018)
  Oleksandr Shchur et al.
  —
• Graph Node-Feature Convolution for Representation Learning (2018)
  Li Zhang et al.
  —
• Hypergraph Convolution and Hypergraph Attention (2019)
  Song Bai et al.
  —
• Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification (2019)
  Fenyu Hu et al.
  —
• Graph Neural Networks for Social Recommendation (2019)
  Wenqi Fan et al.
  —
• Graph Neural Networks for Modelling Traffic Participant Interaction (2019)
  Frederik Diehl et al.
  —
• Heterogeneous Graph Attention Network (2019)
  Xiao Wang et al.
  —
• Learning Discrete Structures for Graph Neural Networks (2019)
  Luca Franceschi et al.
  —
• Just Jump: Dynamic Neighborhood Aggregation in Graph Neural Networks (2019)
  Matthias Fey
  —
• edGNN: a Simple and Powerful GNN for Directed Labeled Graphs (2019)
  Guillaume Jaume et al.
  —