Graphsage
PulseAugur coverage of Graphsage — every cluster mentioning Graphsage across labs, papers, and developer communities, ranked by signal.
2 day(s) with sentiment data
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Network analysis improves unreliable news detection
Researchers have developed a new method for detecting unreliable news domains by analyzing URL-sharing patterns on Telegram. They constructed a domain co-sharing network, revealing that unreliable and reliable news doma…
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New protocol for GNN cross-task transfer reveals directional predictability
Researchers have developed a new protocol to reliably evaluate cross-task transfer in Graph Neural Networks (GNNs) for node classification (NC) and link prediction (LP) tasks. Their findings indicate that transfer from …
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New SKGFusionKAN method enhances IoT network intrusion detection using GNNs and KAN
Researchers have developed a new approach called SKGFusionKAN to improve intrusion detection in Internet of Things (IoT) networks. This method combines graph neural networks (GNNs), specifically GraphSage, with Kolmogor…
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AI system AgentsCAD automates 3D print design modifications
Researchers have developed AgentsCAD, a multi-agent system that uses Large Language Models (LLMs) to automate design modifications for 3D-printed parts. The system analyzes STEP files, identifies potential manufacturing…
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New AgentsCAD system automates FDM part design with LLM reasoning
Researchers have developed AgentsCAD, a novel multi-agent system designed to automate Design for Additive Manufacturing (DFAM) modifications for Fused Deposition Modeling (FDM) parts. The system processes STEP files, id…
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Node embedding dimensionality impacts stability, new arXiv paper finds
A new arXiv paper explores how the dimensionality of node embeddings impacts their stability across different training runs and methods. Researchers found that dimensionality can significantly affect stability, with eff…
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Deep-learning tool rapidly assesses post-hurricane grid damage and schedules repairs
Researchers have developed a two-stage deep-learning tool to expedite post-hurricane damage assessment and repair scheduling for electrical grids. The first stage identifies damaged lines using models like MLP, ResMLP, …
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New GNN approach enhances multi-site pollution prediction accuracy
Researchers have developed a novel approach using Graph Neural Networks (GNNs) to improve the accuracy of particulate matter (PM) pollution prediction. This method dynamically constructs graphs based on inter-class rela…
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Gradient leakage attacks threaten GNNs in circuit design
A new research paper details the first comprehensive evaluation of gradient leakage attacks (GLAs) on graph neural networks (GNNs) used in circuit design and hardware security. The study reveals that GLAs can expose sen…
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New PyTorch CUDA operator speeds up knowledge graph embedding updates
Researchers have developed FuseSampleAgg, a novel PyTorch CUDA operator designed to optimize knowledge graph (KG) embedding updates. This new operator streamlines the neighborhood estimation process by fusing sampling a…
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Graph-based deep learning applied to map generalization tasks
This research paper explores the application of graph-based deep learning to map generalization, specifically for simplifying and aggregating building footprints. The study evaluates graph neural network architectures l…
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Graph Neural Networks enhanced with proximity graphs for dust emission forecasting
Researchers have developed a novel method to enhance Graph Neural Networks (GNNs) for dust source emission forecasting by incorporating proximity graphs. These graphs, including Delaunay triangulation, Gabriel graph, k-…
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Graph Neural Networks Enhance Drone and Cyber Defense in Conflict Zones
A new research paper explores the application of Graph Neural Networks (GNNs) to enhance cybersecurity and drone intelligence, particularly within the context of the Israeli-Iranian conflict. The study proposes an integ…
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New PAMR Model Enhances Prediction of Signed Interactions in Biological Networks
Researchers have developed a new deep graph model called PAMR (polarity-aware multi-relational model) to improve the prediction of signed interactions in biological networks. This model is specifically designed to diffe…
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Spatial Graph Learning Pipeline for Urban Function Inference Detailed
This tutorial demonstrates how to build a spatial graph learning pipeline for urban function inference. It utilizes libraries like city2graph, OSMnx, and PyTorch Geometric to process OpenStreetMap data, construct graph …
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WebKnoGraph framework uses GNNs to optimize website internal linking
Researchers have developed WebKnoGraph, an open-source framework designed to evaluate internal linking strategies for websites. This tool models a website as a graph, uses GraphSAGE to score potential links, and assesse…
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New deep learning model efficiently ranks influential nodes in networks
Researchers have developed a new lightweight deep learning model called 1D-CGS for identifying influential nodes in complex networks. This hybrid model combines 1D convolutional neural networks with GraphSAGE to efficie…
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Researchers seek help with underperforming fraud detection GNN model
A user on Reddit is seeking assistance with a Graph Neural Network (GNN) model designed for fraud detection. Despite implementing feature engineering and constructing a heterogeneous graph using the IEEE CIS Fraud Detec…
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New HetSheaf framework enhances heterogeneous graph learning
Researchers have introduced HetSheaf, a novel framework for learning from heterogeneous graphs by leveraging cellular sheaves. This approach encodes heterogeneity directly into the data structure, allowing for type-awar…
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New hybrid model enhances relational database processing with LLMs and GNNs
Researchers have developed a novel hybrid architecture that combines a fine-tuned BART language model with a GraphSAGE-based Graph Neural Network (GNN) to better process relational database information. This approach ai…