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Network Science

Social network graphs, knowledge graphs, citation networks, molecular graphs for GNN, web link graphs

426 datasets

Network Science

Ablation Performance Metrics for PepLM-GNN Model Variants

Ablation experiment results compare the performance of five PepLM-GNN model variants, including the complete model and four ablated versions. The dataset contains mean and standard deviation for ACC and AUC metrics calculated from five-fold cross-validation. It was created by Ke Yan and published on figshare in March 2026.

Node Feature InteractionGeneralisation PerformanceHybrid Graph NetworkForm Heterogeneous NodesModel Generalisation PerformanceConventional Prediction MethodsExisting Advanced MethodsMarkedly Degraded PerformanceLocal Neighbourhood InformationSemantic Context InformationTrained Language ModelsGraph Isomorphism NetworkCold Start ScenarioComprehensive InformationData Feature CharacterisationInsufficient Generalisation AbilityProtein InteractionEffectively Representing NonPrecise PredictionPeptide Drug Research+1

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Network Science

Statistical T-Test P-Values for PepLM-GNN Comparison

17244 benchmark samples underpin the p-values from statistical t-tests comparing the PepLM-GNN model to other baseline methods. The data reports whether PepLM-GNN's accuracy is statistically significantly higher, based on mean ACC values from five-fold cross-validation. Author Ke Yan published this table on figshare under a CC BY 4.0 license.

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Network Science

Supplementary Material 7 for 31P NMR Prediction with Graph Neural Networks

A 3.6 MB supplementary file published on figshare by Dimitri Domnjuk on 2026-04-27. The file is an IPYNB notebook likely containing code, data, or analysis supporting a research paper on predicting 31P Nuclear Magnetic Resonance signals using a light-weight Graph Neural Network. The specific data content and structure require verification after download.

GraphMachine LearningGraph Neural NetworksComputational ChemistryNuclear magnetic resonance+1

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Network Science

Supplementary Material 8 for GNN Prediction of 31P NMR Signals

Supplementary material 8 from a research article on predicting 31P Nuclear Magnetic Resonance signals using a light-weight Graph Neural Network. The dataset is published on figshare under a CC-BY-4.0 license by author Dimitri Domnjuk and was last updated on 2026-04-27. The specific content and structure of this supplementary file are not detailed in the available metadata.

GraphGraph Neural NetworksComputational ChemistrySupplementary MaterialNmr Spectroscopy+1

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Network Science

Supplementary Graph Data for 31P NMR Signal Prediction via GNN

Supplementary material 6 for a research paper on predicting 31P Nuclear Magnetic Resonance signals using a light-weight Graph Neural Network. The 161.1 MB file, published on figshare by Dimitri Domnjuk under a CC-BY-4.0 license, was last updated on 2026-04-27. The specific content and structure of the data require verification after download.

GraphMachine LearningGraph Neural NetworksComputational ChemistryNmr Spectroscopy+1

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Network Science

Supplementary Data for 31P NMR Prediction with Graph Neural Networks

Supplementary material 2 for a research paper on predicting 31P Nuclear Magnetic Resonance signals using a light-weight Graph Neural Network. The dataset, published on figshare by Dimitri Domnjuk under a CC-BY-4.0 license, is a 258.4 KB IPYNB file. Its last update was recorded on 2026-04-27.

GraphMachine LearningGraph Neural NetworksComputational ChemistryNmr Spectroscopy+1

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Network Science

Supplementary Graph Data for 31P NMR Signal Prediction with GNN

Supplementary material 3 for a research paper on a light-weight Graph Neural Network for predicting 31P Nuclear Magnetic Resonance signals. The dataset is published on figshare by author Dimitri Domnjuk under a CC-BY-4.0 license and was last updated on 2026-04-27. The file is 1.6 MB in size and is provided in an IPYNB (Jupyter Notebook) format.

GraphMachine LearningGraph Neural NetworksComputational ChemistryNmr Spectroscopy+1

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Network Science

Graph Neural Network Model for 31P NMR Signal Prediction

Supplementary material 1 for a research paper on a light-weight Graph Neural Network. The 57.4 MB file, published on figshare by Dimitri Domnjuk, contains a model file for predicting 31P Nuclear Magnetic Resonance signals. It was last updated on 2026-04-27.

GraphMachine LearningGraph Neural NetworksChemistrySupplementary MaterialNuclear magnetic resonance+1

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Network Science

NOAA CYGNSS: Daily 25-km Ocean Wind Speed Measurements from Satellite Constellation

Version 1.2 science-quality wind speed data from the CYGNSS satellite constellation, produced by NOAA/NESDIS. The dataset provides daily netCDF-4 files containing time-tagged and geolocated average wind speeds in 25x25 kilometer grid cells, with a latency of approximately 6 days from the last measurement. It includes updates for high wind speed performance, revised quality flags, and a wind speed retrieval error variable.

Time SeriesGeospatialOcean WindsSatellite Wind SpeedEarth Science Ocean Winds Oceans Surface WindsEarth Science Radar Spectral Engineering Radar CroEarth ScienceEarth Science Ocean Winds Oceans Surface Winds Oce+1

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Network Science

Russia-Ukraine War Twitter/X Interaction Network

A cleaned and anonymized Twitter/X interaction network dataset related to the Russia-Ukraine war. The dataset's author, organization, and specific temporal coverage are unknown. Its size, row count, and file formats are also unspecified.

GraphRussia Ukraine WarSocial MediaTwitter XInteraction Network+1

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Network Science

Moltbook Posts: AI Agent Social Network Discussions

Posts scraped from Moltbook's public API, which requires no authentication. The dataset likely contains discussions from an AI agent social network, featuring fields such as title, content, author, and engagement metrics. It was uploaded by caginb and last updated on May 7, 2026.

TabularTextDiscussionSocial Network+1

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Network Science

Comparison of Hybrid Method vs. SVD and GNN Benchmarks

A 5.5 KB Excel file comparing a proposed hybrid method against Singular Value Decomposition (SVD) and Graph Neural Network (GNN) benchmarks. The dataset was authored by T. Keerthika and last updated on April 13, 2026. It is licensed under CC-BY-4.0 and hosted on figshare.

TabularExcelHybrid MethodGraph Neural NetworkSvd+1

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Network Science

Insurance Fraud Graph Dataset for Fraud Ring Detection

A graph dataset designed for detecting insurance fraud rings. The description indicates it is suitable for graph neural networks, link prediction, and network analysis. The dataset's origin, size, and specific structure are not detailed in the provided metadata.

GraphLink PredictionGraph Neural NetworkInsurance FraudFraud DetectionGraph Network+1

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Network Science

YouTube Interaction Network for Scientific Videos

A merged and anonymized YouTube interaction network dataset. The data likely contains nodes representing users or videos and edges representing interactions, enabling the study of scientific communication patterns. The dataset's origin, size, and specific collection date are unknown.

GraphScientific CommunicationYoutubeSocial Network AnalysisInteraction Graph+1

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Network Science

AGNN JM1: Graph Network Dataset for Machine Learning

AGNN JM1 is a dataset published on Kaggle, likely intended for graph-based machine learning tasks. The title suggests it may contain network data suitable for models like Attentional Graph Neural Networks. Its specific content, size, and origin are not detailed in the available metadata.

GraphMachine LearningGraph Neural NetworksNetwork Analysis+1

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Network Science

Synthetic Jordan Blood Donation Network Dataset

A synthetic dataset modeling a blood donation network in Jordan. It includes entities for donors, hospitals, blood banks, and blood requests. The dataset was created for simulation and modeling purposes, but its author, size, and specific creation date are unknown.

TabularBlood DonationHealthcare NetworkSupply ChainSynthetic DataSynthetic+1

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Network Science

Gnn Disassembly Worldmodel

Real robot disassembly episodes include per-frame constraint graphs, SAM2 segmentation masks, 256D feature embeddings, and full 3D depth information. The dataset was created by Chang Liu of Texas A&M University for a CoRL 2026 project on GNN world models for constraint-aware video generation. It was last updated on Hugging Face on April 11, 2026.

GraphPoint CloudMultimodalGraph Neural NetworksRoboticsComputer VisionRobot Disassembly+1

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Network Science

ROSETTA ALICE: Supplementary Documents and Data for Comet 67P Observations

A small dataset contains CODMAC Level 6 products linked to the Rosetta Orbiter's ALICE UV Spectrometer. It collects supplementary documentation and data of interest to users of the Rosetta Alice data products from the comet phase around comet 67P/Churyumov-Gerasimenko, which took place between 2014-01-21 and 2016-09-30. The dataset was provided by the National Aeronautics and Space Administration and last updated in March 2026.

TextTabularComet 67pPlanetary ScienceUv SpectrometryRosetta Mission+1

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Network Science

AGGNN Datasets: Graph Data for Neural Network Benchmarking

AGGNN Datasets is a collection of graph-structured data published on Kaggle. The title suggests it is intended for use with Attributed Graph Neural Networks or similar models. Specific details on the data's origin, size, and content are not provided in the available metadata.

GraphMachine LearningGraph Neural NetworksBenchmark Datasets+1

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Network Science

ALIGNN Trained Models For JARVIS-DFT Materials Properties

1.7 GB of pre-trained ALIGNN models for predicting properties from the JARVIS-DFT materials database. The models were created by Kamal Choudhary and updated in March 2026. This resource is shared under a CC BY 4.0 license.

JARVIS-DFTALIGNNCondensed Matter Modelling And Density Functional+1

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Network Science Datasets | DataSalon