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Social network graphs, knowledge graphs, citation networks, molecular graphs for GNN, web link graphs
428 datasets
RiTeK is a benchmark for complex reasoning over medical Textual Knowledge Graphs (TKGs). It contains three medical graph QA subsets, including ADint, to evaluate retrieval systems and Large Language Models (LLMs). The dataset was created by ChenAI2015 and last updated on 2026-04-11.
The development of the Antarctic ice shelves during the period 1770-1950 based on expedition maps integrates historical data from maps, charts, and logbooks of Antarctic expeditions. The dataset, compiled by SCIOPS, extends the observational record of ice conditions prior to systematic monitoring starting in the 1950s. It focuses on the Weddell Sea and Ross Sea regions, providing a digital GIS-based overview of floating ice edge dynamics.
News-GNN-v2-importance is a dataset hosted on Kaggle, likely designed for graph-based analysis of news content. The dataset's title suggests it contains importance scores, possibly for nodes or edges within a news article graph. No further metadata is available to confirm its size, origin, or specific structure.
Replication data hosted for the paper 'Are LLM-Enhanced GNNs Privacy-Safe?' by Jimmy Wen. The dataset likely contains graph-structured data used to evaluate privacy risks in hybrid LLM-GNN models. It was last updated on May 6, 2026.
Routing, fault, energy, and traffic records for fog-IoT analysis. The dataset likely contains multiple types of network performance logs for heterogeneous Internet of Things systems. It originates from the Kaggle platform, but specific authorship, size, and update details are not provided.
DSTAGNN-Mihika-Code is a dataset published on Kaggle. The title suggests it relates to a Graph Neural Network implementation or codebase named DSTAGNN. The dataset's specific content, size, and authorship are not detailed in the available metadata.
A dataset published on Kaggle, likely containing graph-structured data for thermal analysis tasks using Graph Neural Networks (GNNs). The dataset's author, organization, size, and specific temporal coverage are unknown. Its content and structure must be verified after download.
A knowledge graph dataset concerning medical consultants, published on Kaggle. The specific entities, relationships, and scale of the graph are unknown from the provided metadata. The original author, organization, and data collection method are also unspecified.
9.5 KB of benchmark results for transductive knowledge graph completion models, including NBFNet and LMKE. The data was compiled by Qingsong Li, with results taken from original papers and some trained with original experimental settings. The dataset was last updated on March 19, 2026.
EvidenceNet provides structured knowledge graphs derived from approximately 500 full-text biomedical articles per disease, published between 2010 and 2025. Created by Chang Zong and released via Harvard Dataverse, this dataset transforms literature into evidence records with normalized entities and quality scores. The public release includes two disease-specific resources: EvidenceNet-HCC and EvidenceNet-CRC.
Hongtu Zhu's Data Management and Sharing Plan outlines the scientific data strategy for constructing comprehensive knowledge graphs for Alzheimer's disease research. The plan describes the data to be generated and used, with a focus on managing and sharing project data. It was last updated on April 27, 2026.
OS MasterMap® Highways Network is described as the most complete, detailed, and accurate navigable road network dataset for Great Britain. It records road dimensions and accessibility, drawing on authoritative sources to support operational decisions. The dataset includes information on planned and roads under construction.
The DeepDive dataset is constructed through automated knowledge graph random walks, entity obfuscation, and difficulty filtering to create challenging questions. It is designed for training deep search agents with complex, multi-step reasoning capabilities. The dataset was created by zai-org and last updated on March 17, 2026.
Ana Costa's dataset supports the publication 'GNN4PPM: Multi-Target Predictive Process Monitoring with Relational Graph Convolutional Networks'. The pipeline combines RDF graph embeddings with relational graph neural networks to predict process attributes. It is designed for multi-target predictive process monitoring tasks, such as next event prediction.
Verified entity and citation dataset for King Pawn USA locations. The dataset appears to be a graph connecting entities and citations related to these retail outlets. It was sourced from Kaggle, but details on its creation, size, and update frequency are unknown.
AYU Publications in Social Networks is a dataset hosted on Kaggle. The dataset's title suggests it contains records related to academic or research publications within the domain of social network analysis. Specific details regarding its size, columns, and creation date are not provided in the available metadata.
A sample cricket knowledge graph links players from the Royal Challengers Bangalore squad with batter-versus-bowler relationships. The dataset appears to model player interactions for network analysis. Its full scale and creation details are not specified.
US Voluntary Observing Ships (VOS) report surface marine observations in real-time and delayed-mode formats. The data is collected via the TurboWin+ e-logbook software, structured in IMMT-5 format, and archived by the National Climatic Data Center. This dataset appears to contain delayed-mode observations retrieved from ship hard drives after port arrival.
Voluntary Observing Ships (VOS) report surface marine observations in both real-time and delayed-mode formats. The data is collected via the TurboWin Version 5.0 e-logbook software, which structures reports in the IMMT-4 format for later retrieval and archiving by the National Climatic Data Center. This dataset represents delayed-mode observations stored on ship hard drives and transmitted after vessels return to port.
Delayed-mode surface marine observations reported by U.S. Voluntary Observing Ships using SEAS v9.1 e-logbook software. Data is structured in the International Maritime Meteorological Tape (IMMT-5) format and archived by the National Climatic Data Center. The temporal coverage and total volume are unspecified.