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https://openreview.net/forum?id=VGsXQOTs1E Reaction Graph Networks for Inorganic Synthesis Condition Prediction of Solid State Materials |... The integration of advanced machine learning (ML) techniques with density functional theory (DFT) has significantly enhanced the optimization and prediction of... graph networks inorganic synthesis https://easychair.org/publications/preprint/Wn1z Directed Graph Networks for Logical Entailment directed graph networks logical entailment https://openreview.net/forum?id=DK4kl9eTIo Boltzmann Graph Networks: Efficient Energy-Based Framework for Graph Representation Learning |... With the rapid growth of interconnected data, graph-structured representations have become essential for modeling complex relational systems. Graph Neural... graph networks efficient energy boltzmann based framework https://openreview.net/forum?id=SRCsyJafgP On Incorporating Scale into Graph Networks | OpenReview Standard graph neural networks assign vastly different latent embeddings to graphs describing the same physical system at different resolution scales. This... graph networks incorporating scale openreview https://openreview.net/forum?id=ytwpZDDOkb Enhancing the Expressivity of Temporal Graph Networks through Source-Target Identification |... Despite the successful application of Temporal Graph Networks (TGNs) for tasks such as dynamic node classification and link prediction, they still perform... graph networks enhancing expressivity temporal https://openreview.net/forum?id=J7Uh781A05p Learning rigid dynamics with face interaction graph networks | OpenReview Face to face, multi-index collisions improve accuracy and efficiency of graph network models for rigid body dynamics interaction graph learning rigid dynamics face https://openreview.net/forum?id=uKZdlihDDn Learning Distributions of Complex Fluid Simulations with Diffusion Graph Networks | OpenReview Physical systems with complex unsteady dynamics, such as fluid flows, are often poorly represented by a single mean solution. For many practical applications,... complex fluid graph networks learning distributions https://openreview.net/forum?id=BjIE_Zfg_Tr&referrer=%5Bthe%20profile%20of%20Martin%20Grohe%5D(%2Fprofile%3Fid%3D~Martin_Grohe1) Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. | OpenReview In recent years, graph neural networks (GNNs) have emerged as a powerful neural architecture to learn vector representations of nodes and graphs in a... higher order graph networks leman go neural https://openreview.net/forum?id=m5RYtApKFOg Curvature-informed multi-task learning for graph networks | OpenReview We analyze loss curvature properties of graph neural networks in the multi-output prediction setting to see why multi-task learning approaches can fail. multi task learning graph networks curvature informed openreview https://openreview.net/forum?id=cLR6FCyMY5k Learning to Solve PDE-constrained Inverse Problems with Graph Networks | OpenReview Solving constrained inverse problem using graph neural networks and generative priors inverse problems graph networks learning solve pde https://openreview.net/forum?id=HSgx1aJeR8 Topological and Temporal Data Augmentation for Temporal Graph Networks | OpenReview Temporal graphs are extensively employed to represent evolving networks, finding applications across diverse fields such as transportation systems, social... data augmentation graph networks topological temporal openreview https://openreview.net/forum?id=RmcPm9m3tnk Generative Scene Graph Networks | OpenReview Human perception excels at building compositional hierarchies of parts and objects from unlabeled scenes that help systematic generalization. Yet most work on... scene graph generative networks openreview https://aclanthology.org/K19-1068/ Memory Graph Networks for Explainable Memory-grounded Question Answering - ACL Anthology Seungwhan Moon, Pararth Shah, Anuj Kumar, Rajen Subba. Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL). 2019. memory graph question answering networks explainable grounded https://ar5iv.labs.arxiv.org/html/2503.11900 [2503.11900] Heterogenous graph neural networks for species distribution modeling Species distribution models (SDMs) are necessary for measuring and predicting occurrences and habitat suitability of species and their relationship with... graph neural networks species distribution 2503 heterogenous modeling https://graphcommons.com/ Graph Commons - Map your networks. Unlock insights. Graph Commons helps you visualize and analyze complex data to reveal hidden patterns, strengthen strategic decisions, and drive innovation—together. graph commons map networks unlock insights https://openreview.net/forum?id=r1lZ7AEKvB The Logical Expressiveness of Graph Neural Networks | OpenReview We characterize the expressive power of GNNs in terms of classical logical languages, separating different GNNs and showing connections with standard notions... graph neural networks logical expressiveness openreview https://deepai.org/publication/artist-similarity-with-graph-neural-networks Artist Similarity with Graph Neural Networks | DeepAI Jul 30, 2021 - 07/30/21 - Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collecti... graph neural networks artist similarity deepai https://openreview.net/forum?id=BJg73xHtvr&referrer=%5Bthe%20profile%20of%20Gary%20B%C3%A9cigneul%5D(%2Fprofile%3Fid%3D~Gary_B%C3%A9cigneul1) Constant Curvature Graph Convolutional Networks | OpenReview We generalize GCNs to (products of) spaces of constant sectional curvature using the gyrovector space formalism. graph convolutional networks constant curvature openreview 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... introduction to graph neural networks comprehensive gnns datacamp https://easychair.org/smart-slide/slide/Dc22 A Gaze into the Internal Logic of Graph Neural Networks, with Logic graph neural networks internal logic gaze https://openreview.net/forum?id=SxRblm9aMs Are Graph Neural Networks Optimal Approximation Algorithms? | OpenReview In this work we design graph neural network architectures that capture optimal approximation algorithms for a large class of combinatorial optimization... graph neural networks approximation algorithms optimal openreview https://openreview.net/forum?id=wk8oXR0kFA&referrer=%5Bthe%20profile%20of%20Iyiola%20Emmanuel%20Olatunji%5D(%2Fprofile%3Fid%3D~Iyiola_Emmanuel_Olatunji1) Releasing Graph Neural Networks with Differential Privacy Guarantees | OpenReview With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over... graph neural networks differential privacy releasing guarantees openreview https://arxiv.org/abs/2211.07823 [2211.07823] Graph Neural Networks for Causal Inference Under Network Confounding Abstract page for arXiv paper 2211.07823: Graph Neural Networks for Causal Inference Under Network Confounding graph neural networks causal inference 2211 https://www.osti.gov/pages/biblio/2005099-accelerating-discrete-dislocation-dynamics-simulations-graph-neural-networks Accelerating discrete dislocation dynamics simulations with graph neural networks (Journal Article)... The U.S. Department of Energy's Office of Scientific and Technical Information graph neural networks dislocation dynamics accelerating discrete simulations https://arxiv.org/abs/2303.07310 [2303.07310] Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks Abstract page for arXiv paper 2303.07310: Learning Reduced-Order Models for Cardiovascular Simulations with Graph Neural Networks https://zenn.dev/hash_yuki/articles/98525a20727c1a Graph Neural Networks graph neural networks https://openreview.net/forum?id=IjMUGuUmBI GraphChef: Decision-Tree Recipes to Explain Graph Neural Networks | OpenReview We propose a new self-explainable Graph Neural Network (GNN) model: GraphChef. GraphChef integrates decision trees into the GNN message passing framework.... graph neural networks decision tree recipes explain openreview https://openreview.net/forum?id=ZuMgYX1irC Combining Graph and Recurrent Networks for Efficient and Effective Segment Tagging | OpenReview Extremely light entity tagging model combining Transformers for text feature extraction, and Graph Neural Networks and recurrent layers for segments interaction recurrent networks combining graph https://openreview.net/forum?id=thV5KRQFgQ Rationalizing and Augmenting Dynamic Graph Neural Networks | OpenReview Graph data augmentation (GDA) has shown significant promise in enhancing the performance, generalization, and robustness of graph neural networks (GNNs).... graph neural networks rationalizing dynamic openreview https://www.kth.se/math/kalender/nik-tavakolian-revealing-the-inner-workings-of-deep-neural-networks-a-graph-based-interpretability-framework-1.1373626?date=2024-12-02&orgdate=2024-05-15&length=1&orglength=0 Nik Tavakolian: Revealing the Inner Workings of Deep neural Networks: A Graph-Based... deep neural networks https://openreview.net/forum?id=fpQojkIV5q8 DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | OpenReview We devise a new GNN variant (DropGNN) with larger expressive power in both theory and practice graph neural networks random dropouts increase https://arxiv.org/abs/2405.20287 [2405.20287] Flexible SE(2) graph neural networks with applications to PDE surrogates Abstract page for arXiv paper 2405.20287: Flexible SE(2) graph neural networks with applications to PDE surrogates graph neural networks https://ignnition.org/ IGNNITION - Fast prototyping of Graph Neural Networks Oct 30, 2024 - IGNNITION is the ideal framework for beginners in neural network programming. Run your own Graph Neural Network model in three simple steps. fast prototyping graph neural networks https://arxiv.org/abs/2307.04937 [2307.04937] Towards Fair Graph Neural Networks via Graph Counterfactual Abstract page for arXiv paper 2307.04937: Towards Fair Graph Neural Networks via Graph Counterfactual graph neural networks 2307 towards fair via https://openreview.net/forum?id=zXgq5lJx8m Robust Learning in Bayesian Parallel Branching Graph Neural Networks: The Narrow Width Limit |... The infinite width limit of random neural networks is known to result in Neural Networks as Gaussian Process (NNGP) (Lee et al. [2018]), characterized by... graph neural networks https://deepai.org/publication/structack-structure-based-adversarial-attacks-on-graph-neural-networks Structack: Structure-based Adversarial Attacks on Graph Neural Networks | DeepAI Jul 23, 2021 - 07/23/21 - Recent work has shown that graph neural networks (GNNs) are vulnerable to adversarial attacks on graph data. Common attack approac... graph neural networks adversarial attacks structure based deepai https://www.uni-augsburg.de/en/vkal/explaining-graph-convolutional-neural-networks-pat Explaining Graph Convolutional Neural Networks: Patient-Specific Subnetworks and Biomarker... convolutional neural networks explaining graph patient specific https://openreview.net/forum?id=mPC9OXGdxx Explaining Graph Neural Networks Using Interpretable Local Surrogates | OpenReview We propose an interpretable local surrogate (ILS) method for understanding the predictions of black-box graph models. Explainability methods are commonly... graph neural networks explaining using local surrogates https://deepai.org/publication/multipath-graph-convolutional-neural-networks Multipath Graph Convolutional Neural Networks | DeepAI May 4, 2021 - 05/04/21 - Graph convolution networks have recently garnered a lot of attention for representation learning on non-Euclidean feature spaces. ... convolutional neural networks multipath graph deepai https://openreview.net/forum?id=fzjd0rW81a- On the Expressive Power of Geometric Graph Neural Networks | OpenReview We propose a Geometric Weisfeiler-Leman test to study the expressive power of geometric graph neural networks. graph neural networks on the expressive power geometric openreview https://openreview.net/forum?id=TYSQYx9vwd Uncertainty Modeling in Graph Neural Networks via Stochastic Differential Equations | OpenReview We propose a novel Stochastic Differential Equation (SDE) framework to address the problem of learning uncertainty-aware representations for graph-structured... graph neural networks uncertainty modeling stochastic differential https://arxiv.org/abs/1009.3499 [1009.3499] Multiplicative Attribute Graph Model of Real-World Networks Abstract page for arXiv paper 1009.3499: Multiplicative Attribute Graph Model of Real-World Networks attribute graph real world 1009 3499 multiplicative https://openreview.net/forum?id=ULQdiUTHe3y Collective Robustness Certificates: Exploiting Interdependence in Graph Neural Networks | OpenReview In tasks like node classification, image segmentation, and named-entity recognition we have a classifier that simultaneously outputs multiple predictions (a... graph neural networks collective robustness certificates exploiting https://openreview.net/forum?id=vxCV1UV7su&referrer=%5Bthe%20profile%20of%20Pascal%20Berrang%5D(%2Fprofile%3Fid%3D~Pascal_Berrang1) Link Stealing Attacks Against Inductive Graph Neural Networks | OpenReview A graph neural network (GNN) is a type of neural network that is specifically designed to process graph-structured data. Typically, GNNs can be implemented in... graph neural networks stealing attacks inductive openreview https://openreview.net/forum?id=t0VbBTw-o8 Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks |... Exploiting the message-passing principle of Graph Neural Networks to certify robustness against strong adversaries that can arbitrarily perturb all features of... gray box randomized message interception smoothing https://openreview.net/forum?id=rkeuAhVKvB Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning | OpenReview We propose to learn an input-dependent subgraph, dynamically and selectively expanded, to explicitly model a sequential reasoning process. message passing https://openreview.net/forum?id=QkRbdiiEjM AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models | OpenReview The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of... graph convolutional networks deep models adaboosting openreview https://openreview.net/forum?id=tZ3JmSDbJM&referrer=%5Bthe%20profile%20of%20Taraneh%20Younesian%5D(%2Fprofile%3Fid%3D~Taraneh_Younesian2) GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks | OpenReview Graph neural networks (GNNs) learn the representation of nodes in a graph by aggregating the neighborhood information in various ways. As these networks grow... graph neural networks grapes learning sample graphs https://deepai.org/publication/ll-gnn-low-latency-graph-neural-networks-on-fpgas-for-particle-detectors LL-GNN: Low Latency Graph Neural Networks on FPGAs for Particle Detectors | DeepAI Sep 28, 2022 - 09/28/22 - This work proposes a novel reconfigurable architecture for low latency Graph Neural Network (GNN) design specifically for particle... graph neural networks https://deepai.org/publication/variational-graph-recurrent-neural-networks Variational Graph Recurrent Neural Networks | DeepAI Aug 26, 2019 - 08/26/19 - Representation learning over graph structured data has been mostly studied in static graph settings while efforts for modeling dyn... recurrent neural networks variational graph deepai https://openreview.net/forum?id=eD534mPhAg Evaluating Post-hoc Explanations for Graph Neural Networks via Robustness Analysis | OpenReview This work studies the evaluation of explaining graph neural networks (GNNs), which is crucial to the credibility of post-hoc explainability in practical usage.... graph neural networks post hoc https://openreview.net/forum?id=FHsEi0qz7t On the Global and Local Calibration of Graph Neural Networks | OpenReview Recent work on calibration of Graph Neural Networks (GNNs) has largely concluded that GNNs are miscalibrated and typically under-confident on standard node... graph neural networks on the global local https://jmlr.org/papers/v26/23-0560.html Improving Graph Neural Networks on Multi-node Tasks with the Labeling Trick graph neural networks https://deepai.org/publication/towards-sparsification-of-graph-neural-networks Towards Sparsification of Graph Neural Networks | DeepAI Sep 11, 2022 - 09/11/22 - As real-world graphs expand in size, larger GNN models with billions of parameters are deployed. High parameter count in such mode... graph neural networks towards deepai https://openreview.net/forum?id=MHQXfiXsr3 On Time Series Clustering with Graph Neural Networks | OpenReview Graph clustering and pooling operators have been adopted in graph-based architectures to capture meaningful patterns in time series data by leveraging both... time series clustering graph neural networks openreview https://openreview.net/forum?id=nUtLCcV24hL Reinforcement Learning Enhanced Explainer for Graph Neural Networks | OpenReview We generate explanations for graph neural networks by a reinforcement learning enhanced method. graph neural networks reinforcement learning enhanced explainer openreview https://openreview.net/forum?id=G0gqWCJjgJf Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity | OpenReview We present a sketch-based GNN training technique with sublinear training time and memory complexities with respect to graph size. graph neural networks sketch gnn scalable https://arxiv.org/abs/2206.11023 [2206.11023] Heterogeneous Graph Neural Networks for Software Effort Estimation Abstract page for arXiv paper 2206.11023: Heterogeneous Graph Neural Networks for Software Effort Estimation graph neural networks for software 2206 heterogeneous effort https://openreview.net/forum?id=SFU-Gk27zX9&referrer=%5Bthe%20profile%20of%20Morteza%20Ramezani%5D(%2Fprofile%3Fid%3D~Morteza_Ramezani1) On the Importance of Sampling in Learning Graph Convolutional Networks | OpenReview Graph Convolutional Networks (GCNs) have achieved impressive empirical advancement across a wide variety of semi-supervised node classification tasks. Despite... graph convolutional networks importance sampling https://openreview.net/forum?id=dxhasYAMQ4 Generalized Reasoning with Graph Neural Networks by Relational Bayesian Network Encodings |... Graph neural networks (GNNs) and statistical relational learning are two different approaches to learning with graph data. The former can provide highly... graph neural networks generalized reasoning https://openreview.net/forum?id=HJ5ILdCHFx Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction |... In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents... graph neural networks https://openreview.net/forum?id=xlhDcKrTVF&referrer=%5Bthe%20profile%20of%20Jiaqi%20Zhu%5D(%2Fprofile%3Fid%3D~Jiaqi_Zhu1) When Do Graph Neural Networks Help with Node Classification: Investigating the Homophily Principle... Homophily principle, i.e. nodes with the same labels are more likely to be connected, has been believed to be the main reason for the performance superiority... graph neural networks https://wandb.ai/syllogismos/machine-learning-with-graphs/reports/19-Applications-of-Graph-Neural-Networks--VmlldzozODUxMzU 19.Applications of Graph Neural Networks 19 applications graph neural networks https://openreview.net/forum?id=PjDJonBo9T Efficient preconditioning for iterative methods with graph neural networks | OpenReview graph neural networks iterative methods efficient preconditioning openreview https://openreview.net/forum?id=Hcr9mgBG6ds Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning |... In this paper, we provide a theory of using graph neural networks (GNNs) for multi-node representation learning (where we are interested in learning a... https://www.datacamp.com/vi/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... introduction to graph neural networks comprehensive gnns datacamp https://openreview.net/forum?id=gxhZj6uvFC Graph Low-Rank Adapters of High Regularity for Graph Neural Networks and Graph Transformers |... We introduce a new low-rank graph adapter, GConv-Adapter, that leverages a two-fold normalized graph convolution and trainable low-rank weight matrices to... for neural networks https://www.datacamp.com/th/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... introduction to graph neural networks comprehensive gnns datacamp https://arxiv.org/abs/2412.01176v1 [2412.01176v1] Superhypergraph Neural Networks and Plithogenic Graph Neural Networks: Theoretical... Abstract page for arXiv paper 2412.01176v1: Superhypergraph Neural Networks and Plithogenic Graph Neural Networks: Theoretical Foundations neural networks 2412 graph theoretical https://hackread.com/tag/graph-neural-networks/ Graph Neural Networks graph neural networks https://openreview.net/forum?id=vzGb4eB7fC&referrer=%5Bthe%20profile%20of%20Zhixin%20Zhou%5D(%2Fprofile%3Fid%3D~Zhixin_Zhou1) Enhancing Trustworthiness of Graph Neural Networks with Rank-Based Conformal Training | OpenReview Graph Neural Networks (GNNs) has been widely used in a variety of fields because of their great potential in representing graph-structured data. However,... graph neural networks https://openreview.net/forum?id=meet41uEs8 Using Causality-Aware Graph Neural Networks to Predict Temporal Centralities in Dynamic Graphs |... Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities... graph neural networks https://arxiv.org/abs/2106.06134v1 [2106.06134v1] Is Homophily a Necessity for Graph Neural Networks? Abstract page for arXiv paper 2106.06134v1: Is Homophily a Necessity for Graph Neural Networks? 2106 homophily necessity graph neural https://deepai.org/publication/transition-to-linearity-of-general-neural-networks-with-directed-acyclic-graph-architecture Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture | DeepAI May 24, 2022 - 05/24/22 - In this paper we show that feedforward neural networks corresponding to arbitrary directed acyclic graphs undergo transition to li... https://www.universiteitleiden.nl/en/research/research-output/science/wireless-random-access-networks-and-spectra-of-random-graph Wireless Random-Access Networks and Spectra of Random Graph - Leiden University This thesis is divided into two parts. In Part I we study metastability properties of queue-based random-access protocols for wireless networks. The network is... random access wireless networks spectra graph https://openreview.net/forum?id=ffElJIzU0B2 Equivariant Graph Hierarchy-based Neural Networks | OpenReview We develop a novel hierarchical structure for equivariant graph networks with expressive message passing. neural networks equivariant graph hierarchy based https://deepai.org/publication/understanding-graph-neural-networks-from-graph-signal-denoising-perspectives Understanding Graph Neural Networks from Graph Signal Denoising Perspectives | DeepAI Jun 8, 2020 - 06/08/20 - Graph neural networks (GNNs) have attracted much attention because of their excellent performance on tasks such as node classifica... graph neural networks understanding signal denoising perspectives https://openreview.net/forum?id=a0mLrqkWyx Gradient Inversion Attack on Graph Neural Networks | OpenReview Graph federated learning is of essential importance for training over large graph datasets while protecting data privacy, where each client stores a subset of... graph neural networks gradient inversion attack openreview https://openreview.net/forum?id=3LzgOQ3eOb Tackling Provably Hard Representative Selection via Graph Neural Networks | OpenReview Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we... graph neural networks tackling hard representative selection https://openreview.net/forum?id=UJXbcJ7qXB&referrer=%5Bthe%20profile%20of%20Taoyu%20Su%5D(%2Fprofile%3Fid%3D~Taoyu_Su1) Hyperbolic-PDE GNN: Spectral Graph Neural Networks in the Perspective of A System of Hyperbolic... Graph neural networks (GNNs) leverage message passing mechanisms to learn the topological features of graph data. Traditional GNNs learns node features in a... graph neural networks https://openreview.net/forum?id=Bylnx209YX Adversarial Attacks on Graph Neural Networks via Meta Learning | OpenReview We use meta-gradients to attack the training procedure of deep neural networks for graphs. graph neural networks adversarial attacks meta learning via openreview https://openreview.net/forum?id=smJ1GcDfIs Graph Wave Networks | OpenReview Dynamics modeling has been introduced as a novel paradigm in message passing (MP) of graph neural networks (GNNs). Existing methods consider MP between nodes... graph wave networks openreview https://arxiv.org/abs/2008.05000 [2008.05000] Degree-Quant: Quantization-Aware Training for Graph Neural Networks Abstract page for arXiv paper 2008.05000: Degree-Quant: Quantization-Aware Training for Graph Neural Networks 2008 degree quant https://www.analyticsvidhya.com/blog/2024/04/mastering-graph-neural-networks-from-graphs-to-insights/ Mastering Graph Neural Networks From Graphs to Insights Apr 15, 2024 - Mastering Graph Neural Networks (GNNs) implementation from basics, applications in social networks, drug discovery, and more. graph neural networks mastering graphs insights https://arxiv.org/abs/2107.13673v2 [2107.13673v2] Relational Graph Neural Networks for Fraud Detection in a Super-App environment Abstract page for arXiv paper 2107.13673v2: Relational Graph Neural Networks for Fraud Detection in a Super-App environment https://www.preprints.org/manuscript/202307.0118 On Addressing the Limitations of Graph Neural Networks[v1] | Preprints.org This report gives a comprehensive summary of two problems about graph convolutional networks (GCNs): over-smoothing and heterophily challenges, and outlines... graph neural networks addressing limitations 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.preprints.org/manuscript/202410.1718 Reliable and Faithful Generative Explainers for Graph Neural Networks[v1] | Preprints.org Graph neural networks (GNNs) have been effectively implemented in a variety of real-world applications, while their underlying work mechanisms remain a... graph neural networks https://deepai.org/publication/can-graph-neural-networks-learn-to-solve-maxsat-problem Can Graph Neural Networks Learn to Solve MaxSAT Problem? | DeepAI Nov 15, 2021 - 11/15/21 - With the rapid development of deep learning techniques, various recent work has tried to apply graph neural networks (GNNs) to sol... graph neural networks learn to solve maxsat problem https://openreview.net/forum?id=hh3salTr27 Fast Temporal Wavelet Graph Neural Networks | OpenReview Spatio-temporal signals forecasting plays an important role in numerous domains, especially in neuroscience and transportation. The task is challenging due to... graph neural networks fast temporal wavelet openreview https://easychair.org/publications/keyword/m83h Keyword: Graph Neural Networks (GNNs) graph neural networks keyword gnns https://openreview.net/forum?id=97GRqCwnJI Training Differentially Private Graph Neural Networks with Random Walk Sampling | OpenReview Deep learning models are known to put the privacy of their training data at risk, which poses challenges for their safe and ethical release to the public.... graph neural networks random walk training private 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=NiMu23k0Ym&referrer=%5Bthe%20profile%20of%20Zenglin%20Xu%5D(%2Fprofile%3Fid%3D~Zenglin_Xu2) Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification |... The message-passing paradigm of Graph Neural Networks often struggles with exchanging information across distant nodes typically due to structural bottlenecks... graph neural networks mitigating https://openreview.net/forum?id=r-oRRT-ElX On Provable Benefits of Depth in Training Graph Convolutional Networks | OpenReview Reveal the discrepancy between the theoretical understanding of over-smoothing and the practical capabilities of GCNs. graph convolutional networks benefits of in training provable depth https://github.com/BUPT-GAMMA/DisC GitHub - BUPT-GAMMA/DisC: NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled... NeurIPS2022-Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure - BUPT-GAMMA/DisC graph neural networks