graph neural networks
PulseAugur coverage of graph neural networks — every cluster mentioning graph neural networks across labs, papers, and developer communities, ranked by signal.
- instance of GNNs and Graph Generative models for biomedical applications 90%
- used by GNNs and Graph Generative models for biomedical applications 70%
- developed Graph Neural Networks (GNNs) 70%
- developed by Graph Neural Networks (GNNs) 70%
- uses finite element method 60%
- affiliated with finite element method 50%
- 2026-05-25 research_milestone Researchers proposed new polynomial-time algorithms for explaining Graph Neural Networks. source
- 2026-05-13 research_milestone A new graph neural network architecture was introduced for the multicut problem. source
- 2026-05-11 research_milestone A new method for pre-training GNNs using ECFPs shows improved performance in QSAR tasks. source
21 day(s) with sentiment data
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New method tackles noisy labels in Graph Neural Networks
Researchers have developed a new method called ICGNN to improve the robustness of Graph Neural Networks (GNNs) when dealing with noisy labels. The approach uses a novel noise indicator, the influence contradiction score…
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New Bayesian Privacy Framework for Graph Neural Networks
Researchers have introduced Bayesian Membership Privacy (BMP), a new framework for assessing privacy in Graph Neural Networks (GNNs). BMP accounts for structural correlations and stochastic training-graph sampling, whic…
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Graph neural network predicts PROTAC protein degradability
Researchers have developed DegradoMap, a new graph neural network designed to predict the degradability of proteins targeted by PROTACs. Unlike previous methods requiring complete PROTAC structures, DegradoMap uses only…
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New RIDE dataset standardizes train delay prediction benchmark
Researchers have introduced RIDE, a new open dataset and benchmark designed to standardize train delay prediction. This nationwide dataset, covering the Belgian railway network from 2023 to 2025, includes 94.5 million t…
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New framework ADPrompt enhances fairness in graph neural networks
Researchers have introduced Adaptive Dual Prompting (ADPrompt), a new framework designed to make Graph Neural Networks (GNNs) fairer. Existing methods often overlook biases present in graph data, leading to unfair outco…
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New GNN model improves Alzheimer's classification using brain network analysis
Researchers have developed a new multi-modal graph neural network designed to improve the classification of preclinical Alzheimer's disease. The model integrates a transformer with a diffusion process to better capture …
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GFFMERGE enables efficient merging of GNN models for simulations
Researchers have developed GFFMERGE, a novel framework for efficiently merging Graph Neural Network (GNN) models used in atomistic simulations. This method addresses the costly retraining required when adapting GNN forc…
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New contrastive learning framework improves graph coloring generalization
Researchers have developed a new contrastive learning framework for graph coloring, a problem central to graph theory with applications in scheduling and resource allocation. This approach aims to create transferable co…
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GNNs analyzed for wireless networks, showing transferability bounds
Researchers have published a theoretical analysis of Graph Neural Networks (GNNs) for wireless communication networks. The study focuses on the transferability of GNNs across different scales, particularly in sparse net…
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New TAGR framework boosts GNN robustness with graph repair
Researchers have introduced Topology-Aware Gaussian Repair (TAGR), a novel framework designed to enhance the robustness of Graph Neural Networks (GNNs). TAGR addresses common issues in real-world graph data, such as noi…
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GNNs and score-based models enhance wireless beamforming with better CSI
Researchers have developed a novel approach for robust hybrid beamforming in wireless communications by leveraging Graph Neural Networks (GNNs) and score-based generative models. This method aims to improve the accuracy…
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New OgBench framework evaluates GNNs on omics data
Researchers have introduced OgBench, a new framework designed to evaluate Graph Neural Networks (GNNs) specifically for omics data. This type of biological data presents a unique challenge where the number of samples is…
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New AdaKernel method learns adaptive kernel parameters for GNNs
Researchers have developed AdaKernel, a novel method for Spatiotemporal Graph Neural Networks (GNNs) that learns adaptive kernel parameters. This approach aims to improve the modeling of spatial dependencies by optimizi…
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RADE technique enhances Graph Neural Networks by adding and dropping edges
Researchers have introduced RADE, a novel technique for Graph Neural Networks (GNNs) designed to combat overfitting and improve the handling of long-range information. Unlike previous methods that focus on either regula…
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New RGVQ framework improves graph representation learning
Researchers have developed RGVQ, a new framework to address codebook collapse in vector quantization for graph representation learning. This issue limits the expressiveness of graph data representations. RGVQ integrates…
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BERT and GNNs enhance historical knowledge graph construction
Researchers have developed a novel system that combines BERT and Graph Neural Networks (GNNs) to construct historical knowledge graphs from traditional texts. This approach effectively addresses linguistic ambiguities a…
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Student explores GNNs for astrophysics research
A computer science student starting at RWTH Aachen is exploring the potential application of Graph Neural Networks (GNNs) in astrophysics research. The student notes that astrophysical data, such as galaxy formation and…
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GNN theory breaks oversmoothing with bifurcation-inspired activations
Researchers have developed a new theoretical framework for Graph Neural Networks (GNNs) that addresses the issue of oversmoothing, a problem where node features become indistinguishable in deep networks. By analyzing ov…
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New Aggregation Buffer Enhances GNN Robustness Beyond DropEdge
Researchers have introduced the Aggregation Buffer, a new parameter block designed to enhance the robustness of Graph Neural Networks (GNNs). This method aims to improve upon DropEdge, a data augmentation technique that…
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New graph condensation methods boost GNN efficiency and scalability
Two new research papers propose novel methods for graph condensation and coarsening, aiming to make Graph Neural Networks (GNNs) more efficient and scalable. The first paper, SP-ESGC, introduces a decoupled approach tha…