gnns - Robuta Search

https://openreview.net/forum?id=7vyGCFTajk Maximally Expressive GNNs for Outerplanar Graphs | OpenReview Most pharmaceutical molecules can be represented as _outerplanar_ graphs. We propose a graph transformation that makes the Weisfeiler-Leman (WL) test and... expressive gnns graphs openreview https://huggingface.co/papers/2404.11568 Paper page - On the Scalability of GNNs for Molecular Graphs Join the discussion on this paper page on the paper scalability gnns molecular https://drive.google.com/file/d/1GAvVhSKpcm51RPiwL0_NYcXYUoIY-eI2/view?usp=sharing Akanksha_Ahuja_GNNs_Remote Sensing_pt1.pdf - Google Drive remote sensing akanksha ahuja gnns pt1 https://www.datacamp.com/ru/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://deepai.org/publication/unveiling-the-role-of-message-passing-in-dual-privacy-preservation-on-gnns Unveiling the Role of Message Passing in Dual-Privacy Preservation on GNNs | DeepAI Aug 25, 2023 - 08/25/23 - Graph Neural Networks (GNNs) are powerful tools for learning representations on graphs, such as social networks. However, their vu... https://arxiv.org/abs/2212.09034v4 [2212.09034v4] Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs... Abstract page for arXiv paper 2212.09034v4: Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs graph neural networks https://arxiv.org/html/2507.13703v1 Binarizing Physics-Inspired GNNs for Combinatorial Optimization physics inspired gnns combinatorial optimization https://arxiv.org/abs/2205.15127v1 [2205.15127v1] Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path... Abstract page for arXiv paper 2205.15127v1: Universal Deep GNNs: Rethinking Residual Connection in GNNs from a Path Decomposition Perspective for Preventing... https://www.amazon.science/publications/boosting-the-performance-of-deployable-timestamped-directed-gnns-via-time-relaxed-sampling Boosting the performance of deployable timestamped directed GNNs via time-relaxed sampling - Amazon... Timestamped graphs find applications in critical business problems like user classification, fraud detection, etc. This is due to the inherent nature of the... https://openreview.net/forum?id=M8wNJcL8tO&referrer=%5Bthe%20profile%20of%20Stefano%20Teso%5D(%2Fprofile%3Fid%3D~Stefano_Teso1) Reconsidering Faithfulness in Regular, Self-Explainable and Domain Invariant GNNs | OpenReview reconsidering faithfulness regular self https://www.datacamp.com/hi/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=onZkYXI7oe AIMing for Standardised Explainability Evaluation in GNNs: A Framework and Case Study on Graph... Graph Neural Networks (GNNs) have advanced significantly in handling graph-structured data, but a comprehensive framework for evaluating explainability remains... https://www.frontiersin.org/research-topics/56183/graph-neural-networks-gnns-foundation-frontiers-and-applications/authors Frontiers | Graph Neural Networks (GNNs): Foundation, Frontiers and Applications Graph structured data such as social networks and molecular graphs are omnipresent in the real world. Developing sophisticated algorithms for representation ... graph neural networks frontiers gnns foundation applications https://arxiv.org/abs/2410.09069v2 [2410.09069v2] Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A... Abstract page for arXiv paper 2410.09069v2: Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating... https://arxiv.org/abs/2405.06849 [2405.06849] GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs Abstract page for arXiv paper 2405.06849: GreedyViG: Dynamic Axial Graph Construction for Efficient Vision GNNs 2405 06849 dynamic axial https://arxiv.org/abs/2509.25570 [2509.25570] AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs Abstract page for arXiv paper 2509.25570: AttentionViG: Cross-Attention-Based Dynamic Neighbor Aggregation in Vision GNNs https://www.datacamp.com/nl/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://www.datacamp.com/ko/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=mubWzUoEzl GNNs Meet Sequence Models Along the Shortest-Path: an Expressive Method for Link Prediction |... Graph Neural Networks (GNNs) often struggle to capture the link-specific structural patterns crucial for accurate link prediction, as their node-centric... https://www.datacamp.com/pl/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=cFsLHAGnyA Dynamic Rescaling for Training GNNs | OpenReview Graph neural networks (GNNs) with a rescale invariance, such as GATs, can be re-parameterized during optimization through dynamic rescaling of network... for training dynamic gnns openreview https://openreview.net/forum?id=klqhrq7fvB On the Scalability of GNNs for Molecular Graphs | OpenReview Scaling deep learning models has been at the heart of recent revolutions in language modelling and image generation. Practitioners have observed a strong... on the scalability gnns molecular graphs https://openreview.net/forum?id=F3kUFcNRWJ Graph Learning at Scale: Characterizing and Optimizing Pre-Propagation GNNs | OpenReview Graph neural networks (GNNs) are widely used for learning node embeddings in graphs, typically adopting a message-passing scheme. This approach, however, leads... at scale graph learning https://openreview.net/forum?id=GfU8DhOzae Beyond Sparse Benchmarks: Evaluating GNNs with Realistic Missing Features | OpenReview Handling missing node features is a critical challenge for deploying Graph Neural Networks (GNNs) in real-world applications such as healthcare and sensor... missing features beyond sparse benchmarks evaluating https://arxiv.org/abs/2502.10818v1 [2502.10818v1] On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging... Abstract page for arXiv paper 2502.10818v1: On Vanishing Gradients, Over-Smoothing, and Over-Squashing in GNNs: Bridging Recurrent and Graph Learning vanishing gradients https://openreview.net/forum?id=94rKFkcm56 Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power | OpenReview The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of... https://github.com/mwcvitkovic/Supervised-Learning-on-Relational-Databases-with-GNNs GitHub - mwcvitkovic/Supervised-Learning-on-Relational-Databases-with-GNNs: Code to reproduce the... Code to reproduce the results in the paper Supervised Learning on Relational Databases with Graph Neural Networks. -... https://openreview.net/forum?id=dqnNW2omZL6 Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs |... Graph neural networks (GNNs), as the de-facto model class for representation learning on graphs, are built upon the multi-layer perceptrons (MLP) architecture... graph neural networks https://openreview.net/forum?id=rIc9adYbH2 GNNs Also Deserve Editing, and They Need It More Than Once | OpenReview Suppose a self-driving car is crashing into pedestrians, or a chatbot is instructing its users to conduct criminal wrongdoing; the stakeholders of such... more than once https://openreview.net/forum?id=EE0eKCE6L1&referrer=%5Bthe%20profile%20of%20Bin%20Liang%5D(%2Fprofile%3Fid%3D~Bin_Liang7) Can GNNs Learn Link Heuristics? A Concise Review and Evaluation of Link Prediction Methods |... This paper explores the ability of Graph Neural Networks (GNNs) in learning various forms of information for link prediction, alongside a brief review of... https://arxiv.org/abs/2111.02671 [2111.02671] GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code... Abstract page for arXiv paper 2111.02671: GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search https://arxiv.org/abs/2410.09069 [2410.09069] Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A... Abstract page for arXiv paper 2410.09069: Explainable AI for Fraud Detection: An Attention-Based Ensemble of CNNs, GNNs, and A Confidence-Driven Gating... https://easychair.org/publications/keyword/m83h Keyword: Graph Neural Networks (GNNs) graph neural networks keyword gnns https://openreview.net/forum?id=fIf2xt4GXZ Machines and Mathematical Mutations: Using GNNs to Characterize Quiver Mutation Classes | OpenReview Machine learning is becoming an increasingly valuable tool in mathematics, enabling one to identify subtle patterns across collections of examples so vast that... https://openreview.net/forum?id=Ic9vRN3VpZ Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction | OpenReview Graph Neural Networks (GNNs) have been widely applied to various fields for learning over graph-structured data. They have shown significant improvements over... graph neural networks https://arxiv.org/abs/2602.09258 [2602.09258] Generalizing GNNs with Tokenized Mixture of Experts Abstract page for arXiv paper 2602.09258: Generalizing GNNs with Tokenized Mixture of Experts 2602 09258 generalizing gnns tokenized https://www.datacamp.com/it/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://www.nvidia.com/en-us/on-demand/session/gtcfall22-a41386/ Accelerate and Scale GNNs with Deep Graph Library and GPUs A41386 | GTC Digital September 2022 |... Graphs play important roles in many applications, including drug discovery, recommender systems, fraud detection, and cybersecurity https://deepai.org/publication/graphworld-fake-graphs-bring-real-insights-for-gnns GraphWorld: Fake Graphs Bring Real Insights for GNNs | DeepAI Feb 28, 2022 - 02/28/22 - Despite advances in the field of Graph Neural Networks (GNNs), only a small number ( 5) of datasets are currently used to evaluat... fake graphs bring real insights https://www.datacamp.com/ro/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=W4Q4SwCjgY Zero-Shot Generalization of GNNs over Distinct Attribute Domains | OpenReview There are no known graph machine learning methods that can zero-shot generalize across attributed graphs with very different node attribute domains and... zero shot generalization gnns distinct attribute https://github.com/graphdeeplearning/benchmarking-gnns GitHub - graphdeeplearning/benchmarking-gnns: Repository for benchmarking graph neural networks... Repository for benchmarking graph neural networks (JMLR 2023) - graphdeeplearning/benchmarking-gnns github benchmarking gnns repository graph https://www.datacamp.com/ja/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=znW5jNIOED Optimizing over trained GNNs via symmetry breaking | OpenReview Optimization over trained machine learning models has applications including: verification, minimizing neural acquisition functions, and integrating a trained... symmetry breaking optimizing trained gnns via https://openreview.net/forum?id=NT9uMRY2Wx The Weisfeiler-Lehman Distance: Reinterpretation and Connection with GNNs | OpenReview lehman distance reinterpretation connection gnns https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1941665&dswid=5709 When GNNs Met a Word Equations Solver : Learning to Rank Equations DiVA portal is a finding tool for research publications and student theses written at the following universities and research institutions. a word gnns met equations solver https://www.datacamp.com/sv/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://drive.google.com/file/d/1kt1_OTP2CTaOWq0TQsPbPtkzU5yPrvQH/view?usp=sharing Michail_GNNs_Remote Sensing_pt2.pdf - Google Drive remote sensing michail gnns pt2 pdf https://openreview.net/forum?id=3YjQfCLdrzz&referrer=%5Bthe%20profile%20of%20Kedar%20Karhadkar%5D(%2Fprofile%3Fid%3D~Kedar_Karhadkar1) FoSR: First-order spectral rewiring for addressing oversquashing in GNNs | OpenReview We propose a graph rewiring algorithm that prevents oversquashing in GNNs via spectral expansion while retaining the original graph via a relational structure... first order spectral rewiring https://openreview.net/forum?id=gSGLkCX9sc&referrer=%5Bthe%20profile%20of%20Xiangyang%20Xue%5D(%2Fprofile%3Fid%3D~Xiangyang_Xue1) Automated Label Unification for Multi-Dataset Semantic Segmentation with GNNs | OpenReview Deep supervised models possess significant capability to assimilate extensive training data, thereby presenting an opportunity to enhance model performance... semantic segmentation automated label unification multi https://openreview.net/forum?id=7vVWiCrFnd Rethinking and Extending the Probabilistic Inference Capacity of GNNs | OpenReview Designing expressive Graph Neural Networks (GNNs) is an important topic in graph machine learning fields. Despite the existence of numerous approaches proposed... rethinking extending probabilistic inference capacity https://www.datacamp.com/zh/tutorial/comprehensive-introduction-graph-neural-networks-gnns-tutorial A Comprehensive Introduction to Graph Neural Networks (GNNs) | DataCamp Learn everything about Graph Neural Networks, including what GNNs are, the different types of graph neural networks, and what they're used for. Plus, learn how... graph neural networks comprehensive introduction gnns datacamp https://openreview.net/forum?id=hFvp9NYfY9&referrer=%5Bthe%20profile%20of%20Fan%20Zhou%5D(%2Fprofile%3Fid%3D~Fan_Zhou11) Redundancy Undermines the Trustworthiness of Self-Interpretable GNNs | OpenReview This work presents a systematic investigation into the trustworthiness of explanations generated by self-interpretable graph neural networks (GNNs), revealing... redundancy trustworthiness self gnns openreview