Latest Graph Neural Networks Research Papers

The newest Graph Neural Networks papers from across the field — arXiv, NeurIPS, CVPR, Nature, and more — refreshed daily and ranked by relevance. Distill AI tracks Graph Neural Networks so you don’t have to: get the standout work delivered to your inbox every morning, with 2-sentence summaries and the option to chat with any paper.

Recent papers

• OncoTraj: a public benchmark for longitudinal resistance prediction in EGFR-mutant non-small-cell lung cancer on osimertinib
  Abhijoy Sarkar, Aarchi Singh Thakur · arXiv · Jun 9, 2026

  Resistance to first-line osimertinib in EGFR-mutant non-small-cell lung cancer (NSCLC) is the canonical example of predictable clonal evolution under therapeutic pressure, yet no public benchmark exists for training or evaluating computatio…

• Graph Neural Network leveraging Higher-order Class Label Connectivity for Heterophilous Graphs
  Takuto Takahashi, Itsuki Nakayama, Takahiro Mitani, Ryosuke Kikuchi et al. · arXiv · Jun 5, 2026

  Node classification in graph neural networks (GNNs) has been widely applied in various fields of graph analysis. GNNs achieve high-accuracy node classification in homophilous graphs, where nodes with the same class label tend to be connecte…

• A graph neural network for the era of large atomistic models
  Duo Zhang, Anyang Peng, Chun Cai, Wentao Li et al. · npj Computational Materials · May 25, 2026

  Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). Scaling laws suggest that their generaliza…

• Efficient Parallelization of Message Passing Neural Network Potentials for Large-Scale Molecular Dynamics
  Junfan Xia, Bin Jiang · JACS Au · May 25, 2026

  Machine learning potentials have achieved great success in accelerating atomistic simulations, among which message passing neural networks (MPNNs) have become increasingly prevalent thanks to their superior accuracy. However, MPNN potential…

• GRAPHLCP: Structure-Aware Localized Conformal Prediction on Graphs
  Peyman Baghershahi, Fangxin Wang, Debmalya Mandal, Sourav Medya · arXiv · May 8, 2026

  Conformal prediction (CP) provides a distribution-free approach to uncertainty quantification with finite-sample guarantees. However, applying CP to graph neural networks (GNNs) remains challenging as the combinatorial nature of graphs ofte…

• Graph Neural Networks in the Wilson Loop Representation of Abelian Lattice Gauge Theories
  Ali Rayat, Gia-Wei Chern · arXiv · May 5, 2026

  Local gauge structures play a central role in a wide range of condensed matter systems and synthetic quantum platforms, where they emerge as effective descriptions of strongly correlated phases and engineered dynamics. We introduce a gauge-…

• Learning Equivariant Neural-Augmented Object Dynamics From Few Interactions
  Sergio Orozco, Tushar Kusnur, Brandon May, George Konidaris et al. · arXiv · May 4, 2026

  Learning data-efficient object dynamics models for robotic manipulation remains challenging, especially for deformable objects. A popular approach is to model objects as sets of 3D particles and learn their motion using graph neural network…

• Turning the TIDE: Cross-Architecture Distillation for Diffusion Large Language Models
  Gongbo Zhang, Wen Wang, Ye Tian, Li Yuan · arXiv · Apr 29, 2026

  Diffusion large language models (dLLMs) offer parallel decoding and bidirectional context, but state-of-the-art dLLMs require billions of parameters for competitive performance. While existing distillation methods for dLLMs reduce inference…

• Do Larger Models Really Win in Drug Discovery? A Benchmark Assessment of Model Scaling in AI-Driven Molecular Property and Activity Prediction
  Jinjiang Guo · arXiv · Apr 29, 2026

  The rapid growth of molecular foundation models and general-purpose large language models has encouraged a scale-centric view of artificial intelligence in drug discovery, in which larger pretrained models are expected to supersede compact …

• STLGT: A Scalable Trace-Based Linear Graph Transformer for Tail Latency Prediction in Microservices
  Yongliang Ding, Qigong Bi, Peng Pu · arXiv · Apr 29, 2026

  Accurate end-to-end tail-latency forecasting is critical for proactive SLO management in microservice systems. However, modeling long-range dependency propagation and non-stationary, bursty workloads while maintaining inference efficiency a…

• A Functorial Formulation of Neighborhood Aggregating Deep Learning
  Sun Woo Park, Yun Young Choi, U Jin Choi, Youngho Woo · arXiv · Apr 27, 2026

  We provide a mathematical interpretation of convolutional (or message passing) neural networks by using presheaves and copresheaves of the set of continuous functions over a topological space. Based on this interpretation, we formulate a th…

• Fraud Detection in Cryptocurrency Markets with Spatio-Temporal Graph Neural Networks
  Lidia Losavio, Luca Persia, Madan Sathe, Dimosthenis Pasadakis · arXiv · Apr 27, 2026

  Technological advancements in cryptocurrency markets have increased accessibility for investors, but concurrently exposed them to the risks of market manipulations. Existing fraud detection mechanisms typically rely on machine learning meth…

• Operational Feature Fingerprints of Graph Datasets via a White-Box Signal-Subspace Probe
  Yuchen Xiong, Swee Keong Yeap, Zhen Hong Ban · arXiv · Apr 24, 2026

  Graph neural networks achieve strong node-classification accuracy, but their learned message passing entangles ego attributes, neighborhood smoothing, high-pass graph differences, class geometry, and classifier boundaries in an opaque repre…

• Gauge-Equivariant Graph Neural Networks for Lattice Gauge Theories
  Ali Rayat, Yaohang Li, Gia-Wei Chern · arXiv · Apr 22, 2026

  Local gauge symmetry underlies fundamental interactions and strongly correlated quantum matter, yet existing machine-learning approaches lack a general, principled framework for learning under site-dependent symmetries, particularly for int…

• F\textsuperscript{2}LP-AP: Fast \& Flexible Label Propagation with Adaptive Propagation Kernel
  Yutong Shen, Ruizhe Xia, Jingyi Liu, Yinqi Liu · arXiv · Apr 22, 2026

  Semi-supervised node classification is a foundational task in graph machine learning, yet state-of-the-art Graph Neural Networks (GNNs) are hindered by significant computational overhead and reliance on strong homophily assumptions. Traditi…

• VLA Foundry: A Unified Framework for Training Vision-Language-Action Models
  Jean Mercat, Sedrick Keh, Kushal Arora, Isabella Huang et al. · arXiv · Apr 21, 2026

  We present VLA Foundry, an open-source framework that unifies LLM, VLM, and VLA training in a single codebase. Most open-source VLA efforts specialize on the action training stage, often stitching together incompatible pretraining pipelines…

• How Embeddings Shape Graph Neural Networks: Classical vs Quantum-Oriented Node Representations
  Nouhaila Innan, Antonello Rosato, Alberto Marchisio, Muhammad Shafique · arXiv · Apr 16, 2026

  Node embeddings act as the information interface for graph neural networks, yet their empirical impact is often reported under mismatched backbones, splits, and training budgets. This paper provides a controlled benchmark of embedding choic…

• High-resolution shape sensing and stress reconstruction for offshore wind-turbine towers: A framework integrating graph neural networks with the inverse finite element method
  Kai Hong, Jiazhen Zhan, Yuhao Guo, Lei Liu et al. · Ocean Engineering · Apr 3, 2026
• Learn from Global Correlations: Enhancing Evolutionary Algorithm via Spectral GNN
  Kaichen Ouyang, Zong Ke, Shengwei Fu, Lingjie Liu et al. · Proceedings of the AAAI Con... · Mar 14, 2026

  Evolutionary algorithms (EAs) are optimization algorithms that simulate natural selection and genetic mechanisms. Despite advancements, existing EAs have two main issues: (1) they rarely update next-generation individuals based on global co…

• TrustGraph: Federated Graph Neural Networks for Cross-Platform Trust and Fraud Propagation Analysis
  Tejas Patel, Arun Kumar, Madhushree Kumari, Rajesh Purushothaman et al. · OpenAlex · Feb 18, 2026

  Centralized fraud detection systems in e-commerce ecosystems face significant limitations due to stringent data privacy regulations, platform heterogeneity, and the distributed nature of sophisticated fraud rings operating across multiple m…

• A graph neural network for the era of large atomistic models
  Duoduo Zhang, Anyang Peng, Chun Cai, Wentao Li et al. · npj Computational Materials · Jun 2, 2025

  Foundation models, or large atomistic models (LAMs), aim to universally represent the ground-state potential energy surface (PES) of atomistic systems as defined by density functional theory (DFT). The scaling law is pivotal in the developm…

• GIGNet: A Graph-in-Graph Neural Network for Automatic Modulation Recognition
  Yang Ke, Wancheng Zhang, Yan Zhang, Haoyu Zhao et al. · IEEE Transactions on Vehicular Technology · Jun 1, 2025

  In this paper, we propose a robust end-to-end classification model, Graph-in-Graph Neural Network (GIGNet), for automatic modulation recognition (AMR). In GIGNet, multi-level graph neural networks (GNNs) are utilized to extract internal gra…

• BS-GAT: a network intrusion detection system based on graph neural network for edge computing
  Yalu Wang, Zhijie Han, Ying Du, Jie Li et al. · Cybersecurity · Apr 24, 2025
• Interpretability study of a typical fault diagnosis model for nuclear power plant primary circuit based on a graph neural network
  Xin Wang, Hang Wang, Min-jun Peng · Reliability Engineering & System Safety · Apr 1, 2025
• Power Distribution Network Topology Detection Using Dual-Graph Structure Graph Neural Network Model
  Afshin Ebtia, Mohsen Ghafouri, Mourad Debbabi, M. Kassouf et al. · IEEE Transactions on Smart Grid · Mar 1, 2025

  Topology detection (TD) in the context of power distribution networks (PDNs) is a fundamental requirement for a wide range of applications, such as fault localization and load management. PDNs suffer from a lack of real-time topological inf…

• Graph Neural Network Enabled Pinching Antennas
  Xinke Xie, Yang Lu, Zhiguo Ding · IEEE Wireless Communications Letters · Feb 8, 2025

  The pinching-antenna system is a novel flexible-antenna technology, capable of both mitigating large-scale path loss and reconfiguring antenna arrays adaptively. Its core principle relies on deploying small dielectric particles along a wave…

• GRLR: Routing With Graph Neural Network and Reinforcement Learning for Mega LEO Satellite Constellations
  Senbai Zhang, Aijun Liu, Chen Han, Xin Xu et al. · IEEE Transactions on Vehicular Technology · Feb 1, 2025

  This paper investigates the routing problem in the mega low earth orbit (mLEO) satellite constellations considering factors including distribution of the users, topology of the networks and dynamics of the inter-satellite links (ISLs). In w…

• Multi‐Distance Spatial‐Temporal Graph Neural Network for Anomaly Detection in Blockchain Transactions
  Shiyang Chen, Yang Liu, Qun Zhang, Zhouhang Shao et al. · Advanced Intelligent Systems · Jan 30, 2025

  This article presents MDST‐GNN, a multi‐distance spatial‐temporal graph neural network for blockchain anomaly detection. To address challenges in detecting fraudulent cryptocurrency transactions, MDST‐GNN integrates a multi‐distance graph c…

• LightGNN: Simple Graph Neural Network for Recommendation
  Guoxuan Chen, Lianghao Xia, Chao Huang · Web Search and Data Mining · Jan 6, 2025

  Graph neural networks (GNNs) have demonstrated superior performance in collaborative recommendation through their ability to conduct high-order representation smoothing, effectively capturing structural information within users' interaction…

• A Novel Hybrid Model for Credit Risk Assessment of Supply Chain Finance Based on Topological Data Analysis and Graph Neural Network
  Kosar Farajpour Mojdehi, Babak Amiri, Amirali Haddadi · IEEE Access · Jan 1, 2025

  Supply Chain Finance (SCF) in the energy sector has emerged as a critical area of focus due to the need for sustainable and efficient financial solutions to manage the complex interactions between various stakeholders, including suppliers, …