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36 results for “Graph Benchmark”
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
A collection of LFR benchmark graphs
<p>This dataset is a collection of undirected and unweighted LFR benchmark graphs as proposed by Lancichinetti et al. [1]. We generated the graphs using the code provided by Santo Fortunato on his personal website [2], embedded in our evaluation framework [3], with two different parameter sets. Let N denote the number of vertices in the network, then</p> <ul> <li> Maximum community size: 0.2N (Set A); 0.1N (Set B)</li> <li> Minimum community size: 0.05N (Set A); 10 (Set B)</li> <li> Maximum node degree: 0.19N (Set A); 0.19N (Set B)</li> <li> Community size distribution exponent: 1.0 (Set A); 1.0 (Set B)</li> <li> Degree distribution exponent: 2.0 (Set A); 2.0 (Set B).</li> </ul> <p>All other parameters assume default values. We provide graphs with different combinations of average degree, network size and mixing parameter for the given parameter sets:</p> <ul> <li>Set A: For average degrees in {15, 25, 50} we provide network sizes in {300, 600, 1200}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs.</li> <li>Set A: For average degrees in {15, 25, 50} we provide mixing parameters in {0.35, 0.45, 0.55}, each with network sizes in {300, 450, 600, 900, 1200, 1800, 2400, 3600, 4800, 6200, 9600, 19200}. For each configuration we provide 50 benchmark graphs.</li> <li>Set B: For average degrees in {20} we provide network sizes in {300, 600, 1200, 2400}, each with 20 different mixing parameters linearly spaced in [0.2, 0.8]. For each configuration we provide 100 benchmark graphs.</li> </ul> <p>Benchmark graphs are given in edge list format. Further, for each benchmark graph we provide ground truth communities as membership list and as structured datatype (.json), its generating random seeds and basic network statistics.</p> <p>[1] Lancichinetti A, Fortunato S, Radicchi F (2008) Benchmark graphs for testing community detection algorithms. Physical Review E 78(4):046110,https://doi.org/10.1103/PhysRevE.78.046110</p> <p>[2] https://www.santofortunato.net/resources, Accessed: 19 Jan 2021</p> <p>[3] https://github.com/synwalk/synwalk-analysis, Accessed: 19 Jan 2021</p>
Large graphs for Gallatin benchmarks
<p>These are the Orkut and cit-Patents graphs taken from the Stanford graph repository. The graphs have been symmetrized and converted into matrix market format for use in the Winter et al. GPU allocator benchmarking survey.</p>
AMiner-534K: Knowledge Graph of AMiner benchmark for Author Name Disambiguation
<p>This dataset is a knowledge graph extracted from a <a href="https://static.aminer.cn/misc/na-data-kdd18.zip">AMiner benchmark</a> for a research project on knowledge graph embeddings (KGEs) for author disambiguation. Structural triples of the knowledge graph are split into training, testing and validation for applying representation learning methods. Textual literals and numeric literals were stored separately in order to implement multimodal approaches for KGEs (see <a href="https://arxiv.org/abs/1802.00934">arXiv:1802.00934</a>). For the same reason, textual literals and numeric literals are already stored into sentence embeddings and a numeric matrix respectively in the files <em>textual_literals.npy </em>and <em>numeric_literals.npy</em>. The file <em>and_eval</em><em>.json </em>contains the evaluation dataset used for evaluating our AND architecture. For the script used to gather this dataset see the GitHub repository: <a href="https://github.com/sntcristian/and-kge/tree/main/aminer">https://github.com/sntcristian/and-kge/tree/main/aminer</a>.</p>
Data for benchmarking graph-based read mappers
<p>Contains all data required for reproducing the experiments in Grytten et. al <strong><em>"Assessing graph-based read mappers against a novel baseline approach highlights strengths and weaknesses of the current generation of methods", </em></strong>including a human genome graphs and one smaller graph for the MCH region.</p>
Topology Bench: Systematic Graph Based Benchmarking for Optical Networks
<p>TopologyBench is a systematic graph theoretical approach to benchmarking optical network topologies. Network datasets are combined with their corresponding graph theoretical analysis to provide a systematic methodology for selecting diverse sets of optical networks for benchmarking. This topology benchmark is comprised of a network dataset and a systematic graph theoretic analysis. The dataset provides (a) 105 real optical networks and (b) synthetic topologies, generated by the SNR-BA model, divided into (i) Syn-small of 900 synthetic networks and (ii) Syn-large of 270,000 synthetic networks. The systematic graph theoretical analysis identifies and analyses structural, spatial and spectral properties of both the real world and synthetic networks. The graph theoretical correlation analysis reveal network design strategies leading to sparse yet efficient networks. An outlier analysis identifies networks that deviate from standard network designs. The analysis also identifies the limitations of real data in terms of network diversity and provides a justification for using synthetic data to complement the real dataset. We conclude the paper by providing a systematic methodology to cluster networks based on unsupervised machine learning and to select a diverse set of topologies for benchmarking. TopologyBench is a novel, high-quality and unified benchmark designed to facilitate research collaborations in long-haul fibre infrastructure by providing a systematic graph theoretical approach to benchmarking optical networks.</p> <p> </p> <p>If you use any of the data provided, please cite our paper:</p> <p> </p> <pre>@article{matzner2024topology, title={Topology Bench: systematic graph-based benchmarking for core optical networks}, author={Matzner, Robin and Ahuja, Akanksha and Sadeghi, Rasoul and Doherty, Michael and Beghelli, Alejandra and Savory, Seb J and Bayvel, Polina}, journal={Journal of Optical Communications and Networking}, volume={17}, number={1}, pages={7--27}, year={2024}, publisher={IEEE} }<br><br></pre>
Results and log of LLM-KG-Bench runs described in article "Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023
<p>Results and log of LLM-KG-Bench runs described in article ""Benchmarking the Abilities of Large Language Models for RDF Knowledge Graph Creation and Comprehension: How Well Do LLMs Speak Turtle?", Frey et al. 2023, to appear in proceedings for workshop DL4KG@ISWC 2023.</p> <p>For data on task FactExtractStatic please contact authors.</p>
Benchmarking datasets used in the manuscript "Strain-level metagenomic profiling using pangenome graphs with PanTax"
Open the record for dataset details and reuse information.
Benchmark datasets for biomedical knowledge graphs with negative statements
<p>We present a collection of datasets for three relation prediction tasks - protein-protein interaction prediction, gene-disease association prediction and disease prediction - that aim at circumventing the difficulties in building benchmarks for knowledge graphs with negative statements. These datasets include data from two successful biomedical ontologies, Gene Ontology and Human Phenotype Ontology, enriched with negative statements. </p>
PheKnowLator Human Disease Knowledge Graph Benchmarks Embeddings -- v1.0.0
<p><strong>PKT Human Disease Knowledge Graph Benchmark Builds - Embeddings (v1.0.0)</strong></p><p><strong>Build Date: September 03, 2019</strong></p><blockquote><p>Please note that all resources linked below redirect to a publicly Google Cloud Storage bucket where all data are publicly accessible. Routing users from this wiki page is perfectly safe and allows us to avoid requiring users to have a Google account and login to download data. If you have any questions or concerns, please email the project maintainer at <a href="https://github.com/callahantiff/PheKnowLator/wiki/callahantiff@gmail.com">callahantiff@gmail.com</a>.</p></blockquote><p>The KG Benchmark Builds can also be downloaded from Zenodo:<br>👉 <strong>KGs:</strong> <a href="https://doi.org/10.5281/zenodo.7030200">https://doi.org/10.5281/zenodo.7030200</a><br>👉 <strong>Embeddings:</strong> <a href="https://zenodo.org/record/7030189">https://zenodo.org/record/7030189</a></p><p> </p><p>A <a href="https://github.com/xgfs/deepwalk-c">modified version</a> of the <a href="https://github.com/phanein/deepwalk">DeepWalk algorithm</a> was implemented to generate molecular mechanism embeddings from the biomedical knowledge graph. A t-SNE plot of the dimensionality reduced mechanism embeddings is shown in <a href="https://github.com/callahantiff/PheKnowLator/wiki/v1.0.0/figure-2-t-sne-plot-of-molecular-mechanisms">Figure</a>. For this release, the hyperparameters were set to 512 dimensions, 100 walks, walk length of 20, and a window of 10. Two types of KGs were embedded: (1) the full KG; and (2) the full KG with deductive closure using the OWL 2 EL reasoner, ELK via Protégé v5.1.1. ELK is able to classify instances and supports inferences over class hierarchies and object properties. inference over disjointness, intersection, and existential quantification (ontology class hierarchies).</p>
Wikidata Graph Pattern Benchmark (WGPB) for RDF/SPARQL
<p>The Wikidata Graph Pattern Benchmark (WGPB) is a benchmark consisting of 50 instances of 17 different abstract query patterns giving a total of 850 SPARQL queries. The goal of the benchmark is to test the performance of query engines for more complex basic graph patterns. The benchmark was designed for evaluating worst-case optimal join algorithms but also serves as a general-purpose benchmark for evaluating (basic) graph patterns. The queries are provided in <a href="https://www.w3.org/TR/sparql11-query/">SPARQL syntax</a> and all return at least one solution. We limit the number of results returned to a maximum of 1,000.</p> <p><strong>Queries</strong></p> <p>We provide an example of a "square" basic graph pattern (comments are added here for readability):</p> <pre><code class="language-sql">SELECT * WHERE { ?x1 <http://www.wikidata.org/prop/direct/P149> ?x2 . # architectural style ?x2 <http://www.wikidata.org/prop/direct/P1269> ?x3 . # facet of ?x3 <http://www.wikidata.org/prop/direct/P156> ?x4 . # followed by ?x1 <http://www.wikidata.org/prop/direct/P135> ?x4 . # movement } LIMIT 1000</code></pre> <p>There are 49 other queries similar to this one in the dataset (replacing the predicates with other predicates), and 50 queries for 16 other abstract query patterns. For more details on these patterns, we refer to the publication mentioned below.</p> <p>Note that you can try the queries on the public <a href="https://query.wikidata.org/">Wikidata Query Service</a>, though some might give a timeout.</p> <p><strong>Generation</strong></p> <p>The queries were generated over a reduced version of the <a href="https://www.mediawiki.org/wiki/Wikibase/Indexing/RDF_Dump_Format#Truthy_statements">Wikidata truthy dump</a> from November 15, 2018 that we call the Wikidata Core Graph (WCG). Specifically, in order to reduce the data volume, multilingual labels, comments, etc., were removed as they have limited use for evaluating joins (English labels were kept under <em>schema:name</em>). Thereafter, in order to facilitate the generation of the queries, triples with rare predicates appearing in fewer than 1,000 triples, and very common predicates appearing in more than 1,000,000 triples, were removed. The queries provided will generate the same results over both graphs.</p> <p><strong>Files</strong></p> <p>In this dataset, we then include three files:</p> <ul> <li><strong>wgpb-queries.zip </strong>The list of 850 queries</li> <li><strong>wikidata-wcg.nt.gz </strong>Wikidata truthy graph with English labels</li> <li><strong>wikidata-wcg-filtered.nt.bz2 </strong>Wikidata truthy graph with English labels filtering triples with rare (<1000 triples) and very common (>1000000) predicates</li> </ul> <p><strong>Code</strong></p> <p>We provide <a href="https://cirojas.github.io/leapfrog-benchmark/">the code</a> for generating the datasets, queries, etc., along with scripts and instructions on how to run these queries in a variety of SPARQL engines (Blazegraph, Jena, Virtuoso and our worst-case optimal variant of Jena), .</p> <p><strong>Publication</strong></p> <p>The benchmark is proposed, described and used in the following paper. You can find more details about how it was generated, the 17 abstract patterns that were used, as well as results for prominent SPARQL engines.</p> <ul> <li>Aidan Hogan, Cristian Riveros, Carlos Rojas and Adrián Soto. "<a href="http://aidanhogan.com/docs/SPARQL_worst_case_optimal.pdf"><em>A Worst-Case Optimal Join Algorithm for SPARQL</em></a>". In the Proceedings of the 18th International Semantic Web Conference (ISWC), Auckland, New Zealand, October 26–30, 2019.</li> </ul>
Benchmarking datasets used in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs"
<p>This is the raw long-read sequencing data used for benchmarking experiments in the manuscript "VeChat: Correcting errors in noisy long reads using variation graphs". The name of the dataset is labeled as : simulated/real; ecoli/metagenome; ploidy/name; pacbio/ont; sequencing error rate; average sequencing coverage per haplotype.</p>
The MAPLE Benchmark for Graph Mining
<p>This repository contains the graph format of MAPLE (a large-scale collection of scientific papers across 19 scientific fields), which can serve as a comprehensive evaluation benchmark for graph mining tasks (e.g., node classification, link prediction) in the scientific domain.</p> <p>MAPLE was introduced in the WWW 2023 paper "The Effect of Metadata on Scientific Literature Tagging: A Cross-Field Cross-Model Study", available at <a href="http://arxiv.org/abs/2302.03341">https://arxiv.org/abs/2302.03341</a>.</p> <p>The original format of MAPLE used for text mining tasks (e.g., multi-label text classification) can be found at <a href="http://zenodo.org/record/7611544">https://zenodo.org/record/7611544</a>.</p> <p>Please refer to <a href="http://github.com/yuzhimanhua/MAPLE">https://github.com/yuzhimanhua/MAPLE</a> for more details on the data format.</p> <p>If you find MAPLE useful, please cite our paper:</p> <pre><code>@inproceedings{zhang2023effect, title={The effect of metadata on scientific literature tagging: A cross-field cross-model study}, author={Zhang, Yu and Jin, Bowen and Zhu, Qi and Meng, Yu and Han, Jiawei}, booktitle={WWW'23}, pages={1626--1637}, year={2023} }</code></pre>
Restricted corpora used in the benchmarks of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora"
<p>Corpora used in the evaluation of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora" (Thomas Krause 2018, Humboldt-Universität zu Berlin) which access is limited to academic research purposes. This includes the following corpora in the relANNIS format:</p> <ul> <li> <p>Kobalt-DaF-Korpus (not published under an open license yet, https://www.linguistik.hu-berlin.de/de/institut/professuren/korpuslinguistik/forschung/kobalt-daf)</p> </li> <li> <p>TIGER (http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/tiger.html)</p> </li> <li> <p>TüBa-D/Z (http://www.sfs.uni-tuebingen.de/ascl/ressourcen/corpora/tueba-dz.html)</p> </li> </ul> <p>The original TIGER corpus can be downloaded from http://www.ims.uni-stuttgart.de/forschung/ressourcen/korpora/TIGERCorpus/license/htmlicense.html and the TüBa-D/Z is available after manually requesting a download-link at http://www.sfs.uni-tuebingen.de/ascl/ressourcen/corpora/tueba-dz.html</p> <p>Access to both the TIGER and TüBa-D/Z corpora is restricted by the original authors and you need to accept licenses that you are solely using the corpus for academic research. Since the original providers don't include versions in the relANNIS format, it has been made available as restricted download here.</p> <p> </p> <p> </p>
Dump of RDF dataset used by PO for a Graph Database benchmark, 2022
<p>This dataset represents a newer version of the NQUADS files in RDF from Publication Offices used for benchmarking graph databases. </p> <p> </p>
Corpora used in the benchmarks of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora"
<p>Corpora used in the evaluation of the PhD thesis "ANNIS: A graph-based query system for deeply annotated text corpora" (Thomas Krause 2018, Humboldt-Universität zu Berlin) which have some form of license that allows their redistribution. See LICENSE.txt in each folder for the actual license of each corpus and the original authors.</p> <p>Since each corpus has a different license, the access is restricted.</p> <p><strong>If using one of the corpora please cite the original authors mentioned in the LICENSE.txt!</strong></p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.