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12 results for “Subgraphs”
Subgraphs of functional brain networks identify dynamical constraints of cognitive control
<p>Post-processed BOLD fMRI functional connectivity data from human subjects performing two distinct cognitive control tasks.</p> <p>See enclosed README file for information regarding data organization and handling.</p>
[Artifact] Qubit Allocation as a Combination of Subgraph Isomorphism and Token Swapping
<p>Object-Oriented Programming, Systems, Languages & Applications (OOPSLA'19) artifact for the paper: "Qubit Allocation as a Combination of Subgraph Isomorphism and Token Swapping".</p>
CollabRec: DBpedia Subgraphs (2022-09)
<p>The core version of DBpedia has too many entities and statements to train recommendation models in a reasonable time frame, which is why we created two subsets (DB1M, and DBA240) of the core version of DBpedia from September 2022.</p> <p><strong>File structure</strong><br> Each dataset is located in their own folder with the following files:</p> <ul> <li><em>index.tsv.gz</em> is a file in tabular format that maps a simple integer to a URI, which identifies an entity in the KG.</li> <li><em>index_labels.tsv.gz</em> is a file that links entities (represented by their index number) to their label and description.</li> <li><em>relevant_entities.tsv.gz</em> is a file with all the entities, which occur as subject or/and as object in statements of the subsampled KG.</li> <li><em>statements.tsv.gz</em> is a file with all the statements of the subsampled KG. The first column contains the subjects, second column the predicates, and the third column the objects. All those entities are represented by their index number (see <em>index.tsv.gz</em>) and not their URI.</li> <li><em>statements.nt.gz</em> is a file with all the statements of the subsampled KG in N-Triples format.</li> </ul>
Discrete Feature Representations of CHO Reaction Mechanisms as Quasireaction Subgraphs
<p>This data set contains 194778 quasireaction subgraphs extracted from CHO transition networks with 2-6 non-hydrogen atoms (CxHyOz, 2 <= x + z <= 6).</p> <p>The complete table of subgraphs (including file locations) is in CHO-6-atoms-subgraphs.csv file. The subgraphs are in GraphML format (http://graphml.graphdrawing.org) and are compressed using bzip2. All subgraphs are undirected and unweighted. The reactant and product nodes (initial and final) are labeled in the "type" node attribute. The nodes are represented as multi-molecule SMILES strings. The edges are labeled by the reaction rules in SMARTS representation. The forward and backward reading of the SMARTS string should be considered equivalent.</p> <p>The generation and analysis of this data set is described in<br> D. Rappoport, Statistics and Bias-Free Sampling of Reaction Mechanisms from Reaction Network Models, 2023, submitted. Preprint at ChemrXiv, DOI: 10.26434/chemrxiv-2023-wltcr</p> <p>Simulation parameters<br> - CHO networks constructed using polar bond break/bond formation rule set for CHO.<br> - High-energy nodes were excluded using the following rules:<br> (i) more than 3 rings, (ii) triple and allene bonds in rings, (iii) double bonds at<br> bridge atoms,(iv) double bonds in fused 3-membered rings.<br> - Neutral nodes were defined as containing only neutral molecules.<br> - Shortest path lengths were determined for all pairs of neutral nodes.<br> - Pairs of neutral nodes with shortest-path length > 8 were excluded.<br> - Additionally, pairs of neutral nodes connected only by shortest paths passing through<br> additional neutral nodes (reducible paths) were excluded.</p> <p>For background and additional details, see paper above.</p>
Wikidata Thematic Subgraph Selection
<p><strong>Wikidata Thematic Subgraph Selection</strong></p> <p>These datasets have been designed to train and evaluate algorithms to select thematic subgraphs of interest in a large knowledge graph from seed entities of interest. Specifically, we consider Wikidata. Given a set of seed QIDs of interest, a graph expansion is performed following P31, P279, and (-)P279 edges. Traversed classes that thematically deviates from seed QIDs of interest should be pruned. Datasets thus consist of classes reached from seed QIDs that are labeled as "to prune" or "to keep".</p> <p><strong>Available datasets</strong></p> <table> <tbody><tr> <th>Dataset</th> <th># Seed QIDs</th> <th># Labeled decisions</th> <th># Prune decisions</th> <th>Min prune depth</th> <th>Max prune depth</th> <th># Keep decisions</th> <th>Min keep depth</th> <th>Max keep depth</th> <th># Reached nodes up</th> <th># Reached nodes down</th> </tr> </tbody><tbody> <tr> <td><a href="data/dataset1">dataset1</a></td> <td>455</td> <td>5233</td> <td>3464</td> <td>1</td> <td>4</td> <td>1769</td> <td>1</td> <td>4</td> <td>1507</td> <td>2593609</td> </tr> <tr> <td><a href="data/dataset2">dataset2</a></td> <td>105</td> <td>982</td> <td>388</td> <td>1</td> <td>2</td> <td>594</td> <td>1</td> <td>3</td> <td>1159</td> <td>1247385</td> </tr> </tbody> </table> <p>Each dataset folder contains</p> <ul> <li><code>datasetX.csv</code>: a CSV file containing one seed QID per line (not the complete URL, just the QID). This CSV file has no header.</li> <li><code>datasetX_labels.csv</code>: a CSV file containing one seed QID per line and its label (not the complete URL, just the QID)</li> <li><code>datasetX_gold_decisions.csv</code>: a CSV file with seed QIDs, reached QIDs, and the labeled decision (1: keep, 0: prune)</li> <li><code>datasetX_Y_folds.pkl</code>: folds to train and test models based on the labeled decisions</li> </ul> <p><code>dataset1-2</code> consists of using <code>dataset1</code> for training and <code>dataset2</code> for testing.</p> <p><strong>License</strong></p> <p>Datasets are available under the <a href="https://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC</a> license.</p>
CG:Shop 2022 Results: SAT-Based Local Search for Plane Subgraph Partitions
<p>The results for the CG:SHOP 2022 challenge achieved by team tuwien.</p>
Metadata of MALBA h-index subgraph
<p>Dataset for the QSS publication "Exploring publication networks with a local cohesion-maximizing algorithm" (2024) by Held / Gläser.</p> <p>It contains the UT-List and selected metadata of the subgraph that resulted from running MALBA with a seed of seven highly cited h-index publications, resulting in a subgraph of 1,018 publications. The file is directly taken from MALBA's output.</p> <p>Columns read: UT - Title - Year - Authors - Journal - in_step_added_by_MALBA - in_surrounding_of_MALBA_subgraph?</p> <p>Data were obtained from the stable version of the Web of Science database of the Competence Centre for Bibliometrics (version January 2023, https://bibliometrie.info/).</p> <p> </p>
The figure of the construction of the subgraphs
Open the record for dataset details and reuse information.
Practical Bigraphs via Subgraph Isomorphism - Benchmark Instances
<p>A set of 11176 test instances for comparing the existing SAT solver found in BigraphER with the new Subgraph based solver using the Glasgow subgraph solver.</p> <p>See README for details.</p> <p>Also included in results.tar.xz are those presented in the paper.</p>
Subgraph Matching
<p>Datasets for "Detecting Small Query Graphs in A Large Graph via Neural Subgraph Search".</p>
Subgraph Matching Datasets
<p>Subgraph Matching datasets.</p>
Maximum Common Subgraph Detection
<p>Maximum Common Subgraph Detection.</p>
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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.