#graph neural networks
82 resultsFully Inductive Cardinality Estimation
Tim Schwabe, Lukas Ketzer, Maribel Acosta
The paper introduces FICE, a graph neural network‑based estimator that can predict the cardinalities of SPARQL Basic Graph Pattern queries on knowledge graphs it has never seen bef…
Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
Qingzhao Zhang
The paper examines realistic, targeted adversarial attacks on graph-based traffic forecasting models and proposes a physics‑informed detection‑based defense that improves robustnes…
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá +2
The paper proposes training graph neural networks on geometrically renormalized, coarse‑grained versions of a graph and then directly applying the learned weights to the original f…
Persistent Gaussian Perturbations Prevent Oversmoothing in Recurrent Graph Neural Networks
Mostafa Haghir Chehreghani
The paper shows that injecting independent Gaussian noise after each step of a recurrent graph neural network creates a stochastic dynamical system that remains diverse, proving ma…
Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors
Neelam Akula, Surbhi Kumar, Murat Kantarcioglu +1
The paper defines a clean evaluation protocol for transferring knowledge between node classification and link prediction on the same graph, shows that transfer is directionally dep…
Deep Learning for Accelerated Long-Horizon Forecasting of Multicomponent Multiphase Microstructure Evolution in High-Entropy Alloys
Hamidreza Razavi, Nele Moelans
The paper introduces a surrogate model combining autoencoders, graph convolutional networks, and LSTM to rapidly predict long‑term microstructure evolution in multicomponent high‑e…
Oracle-Budgeted Molecular Optimization with Short-Term Graph Memory
Jiannan Yang, Veronika Thost, Xiang Ling +1
The paper proposes a short-term graph memory module that uses an online graph neural surrogate to pre‑screen candidate molecules, allowing a fixed oracle budget to be spent on high…
Data-free neural PDE solvers based on Graph Neural Networks and weak forms
Mikel M. Iparraguirre, Iciar Alfaro, David Gonzalez +1
The paper introduces a neural network that solves partial differential equations without any training data by using a graph neural network and the weak form of the equations, compu…
A foundation model of numerical intelligence with cross-disciplinary generalization
Chenghan Wu, Zongmin Yu, Liu Yang
The paper introduces UNICON, a foundation model that learns predictive relations from graph‑based numerical contexts and can apply this numerical intelligence across scientific and…
Dynamic Spectral Filtering for Temporal Graph Learning: Learning Evolving Propagation Operators
Yan Kong
The paper introduces Dynamic Spectral Filtering (DSF), a method that lets the graph propagation operator evolve over time using Chebyshev polynomial filters with time‑dependent coe…
TopoFormer: Topology Meets Attention for Graph Learning
Md Joshem Uddin, Astrit Tola, Cuneyt Gurcan Akcora +1
The paper introduces TopoFormer, a framework that converts graph topology into ordered token sequences via a Topo-Scan module and processes them with a Transformer to obtain effici…
Graph Neural Network Force Fields for Spin Dynamics in Metallic Magnets
Ali Rayat, Yunhao Fan, Gia-Wei Chern
The paper presents a graph neural network framework that learns magnetic force fields from electronic calculations to efficiently simulate spin dynamics in metallic magnets, reprod…
Semi-Supervised Learning for Molecular Graphs via Ensemble Consensus
Rasmus Tirsgaard, Laurits Fredsgaard, Marisa Wodrich +2
The paper proposes a semi-supervised learning approach for molecular graph data that uses an ensemble consensus objective to improve prediction accuracy, robustness, and calibratio…
Graph Neural Multilevel Preconditioners for Iterative Solvers
Zechen Zhang, Rui Peng Li, Yousef Saad
The paper proposes a Graph Neural Multilevel Preconditioner that integrates an algebraic multigrid hierarchy into a learned GNN framework to improve the convergence of iterative so…
FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning
Zekai Chen, Haodong Lu, Shihao Li +5
The paper introduces FedOGL, a framework for federated multimodal graph learning that mitigates catastrophic forgetting by preserving semantic and structural memory through client-…
Stimulus-Evoked Network Dynamics in Human Cortical Organoids: From a Graph-Computational Framework to Repeated-Stimulation Depression
Esmaeil S. Nadimi, Vinay C. Gogineni, Jan-Matthias Braun +3
The paper presents a graph‑based computational approach to analyze how electrical stimuli trigger activity in human cortical organoids recorded with high‑density MEA, finding that…
DualG-MRAG: Decoupling Macro-Reasoning and Micro-Matching for Multimodal Retrieval-Augmented Generation
Jiacheng Tao, Qingyun Sun, Haonan Yuan +2
The paper introduces DualG-MRAG, a framework that separates global reasoning and fine-grained evidence matching using macro and micro graphs to improve multimodal retrieval-augment…
Learning to Trace Seiberg Dualities
Jonathan J. Heckman, Shani Meynet, Alessandro Mininno +1
The paper applies machine learning, including transformers and MLPs, to identify Seiberg dualities in supersymmetric quiver gauge theories by learning quiver mutations, showing imp…
AgentGFM: A Graph Foundation Model with Node-Agent Information-Flow Control
Jingbo Cui, Jitao Zhao, Di Jin +1
The paper introduces AgentGFM, a graph foundation model where each node acts as an agent that autonomously decides how to propagate information using a trainable policy, enabling a…
Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
Yinan Gao, Jiarong Xu, Xiaohang Zhao +1
The paper introduces MalGuard, a graph‑based malware detection system that groups basic blocks into operational roles and learns expressive program‑graph representations to improve…
Embedding Items at Scale: Comparing GNN-Based and ID-Based Item Embeddings in the Yandex Ecosystem
Sergei Makeev, Artem Matveev, Vladimir Baikalov +1
The paper compares pretrained graph neural network item embeddings with end‑to‑end trainable embeddings in transformer‑based sequential recommendation systems at Yandex, finding pr…
Examining the Efficacy of Graph Neural Network Message-Passing in Regression Contexts
Keith G. Mills, Aedan J. DeFrates, Joong Ho Kim
The paper evaluates how different graph neural network message‑passing layers perform on scalar regression tasks, finding that deep convolutional GNNs like GEN generally outperform…
When Do Learned Diffusion Proposals Help Constraint Solving? A Controlled Study on Continuous Algebraic Systems
Quang Bui, Sparsh Roy, Akash Gundimeda +1
The paper studies when graph‑neural diffusion proposals improve solving continuous algebraic constraint systems, comparing learned proposals to random multi‑start baselines and ide…
Neural Architecture Search for Traffic Prediction: A Survey of Methods, Challenges, and Future Directions
Truong Giang Vu, Li Yang, Richard W. Pazzi
The paper surveys how neural architecture search techniques are used to automatically design deep learning models for traffic prediction, reviewing gradient‑based, evolutionary, an…