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15 results for “graph clustering”
Datasets for Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction
<p>This dataset supplements the article “<a href="https://doi.org/10.1162/COLI_a_00354">Watset: Local-Global Graph Clustering with Applications in Sense and Frame Induction</a>” published in the Computational Linguistics journal:</p> <ul> <li> <p><code>watset-coli-lcc-performance.tsv</code>: runtime analysis</p> </li> <li> <p><code>watset-coli-synsets.zip</code>: synset induction experiment (note that <code>pairwise-{en-babelnet,ru-rwn}.pkl</code> files are excluded due to the licensing issues)</p> </li> <li> <p><code>watset-coli-triframes.zip</code>: semantic frame induction experiment</p> </li> <li> <p><code>watset-coli-classes.zip</code>: semantic class induction experiment</p> </li> </ul>
Space of Optimal Solutions of the Correlation Clustering Problem for Complete Signed Graphs
<p><strong>Description. </strong>This is the data used in the experiments of the following paper:</p> <ul> <li>N. Arınık, R. Figueiredo, and V. Labatut, “Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs,” <em>Journal of Complex Networks </em>8(6):cnaa025, 2020. DOI: <a href="http://doi.org/10.1093/comnet/cnaa025">10.1093/comnet/cnaa025</a> ⟨<a href="https://hal.archives-ouvertes.fr/hal-02994011">hal-02994011</a>⟩</li> </ul> <p>This dataset contains:</p> <ul> <li>Plot files used in the article;</li> <li>Input signed networks;</li> <li>All optimal solutions (i.e. optimal solution space) of the corresponding networks;</li> <li>Evaluation files.</li> </ul> <p><strong>Source code. </strong>The code source is accessible on GitHub: <a href="https://github.com/CompNet/Sosocc">https://github.com/CompNet/Sosocc</a></p> <p><strong>Citation. </strong>If you use the data or source code, please cite the above article.</p> <p><br><code>@Article{Arinik2020,</code><br><code> author = {Arınık, Nejat and Figueiredo, Rosa and Labatut, Vincent},</code><br><code> title = {Multiplicity and Diversity: Analyzing the Optimal Solution Space of the Correlation Clustering Problem on Complete Signed Graphs},</code><br><code> journal = {Journal of Complex Networks},</code><br><code> year = {2020},</code><br><code> volume = {8},</code><br><code> number = {6},</code><br><code> pages = {cnaa025},</code><br><code> doi = {10.1093/comnet/cnaa025},</code><br><code>}</code><br><br></p> <p>--------------------------------------------</p> <p><strong>Details.</strong></p> <p><br><strong># PLOT FILES</strong><br>* `<em>Figure1.zip</em>`: Figures showing that there might be many distinct optimal solutions of a small-sized network.<br>* `<em>Figure2.zip</em>`: Figures showing that distinct optimal solutions of a given network might be partition-wise very similar or different.<br>* `<em>Figure4: All Results.zip</em>`: Figure 4 in the article contains only a few plots regarding the results for space considerations. This zip file contains all plots, and it is organized by the values of `<em>l<sub>0</sub></em>`. In each `<em>l<sub>0</sub></em>` folder, the results are shown in three different perspectives:<br>--- Detected Imbalance Percentage vs Graph Order (i.e. number of vertices)<br>--- Prop mispl vs Graph order<br>--- Graph order vs Prop mispl<br>* `<em>workflow.pdf</em>`: The workflow of the methodology used in the article.<br>* `<em>Syrian network With All Solutions.pdf</em>`: Syrian network (on top) with core part information through node colors, and its optimal solutions in which node colors represent partition information (on bottom).<br> </p> <p><strong>#NETWORKS</strong><br>All networks are in `<em>Input Signed Networks.tar.gz</em>`.<br>Networks are generated through a simple random model (available in <em>https://github.com/CompNet/SignedBenchmark</em>) designed to produce complete (or uncomplete) unweighted networks with built-in modular structure.<br>There are 3 parameters used for the generation:</p> <ol> <li>number of nodes (`<em>n</em>`)</li> <li>initial number of modules (`<em>l<sub>0</sub></em>`)</li> <li>proportion of misplaced links, i.e. proportion of frustrated links, (`<em>q<sub>m</sub></em>`)</li> </ol> <p>Inside `<em>Input Signed Networks.tar.gz</em>`:<br>NETWORKS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>The number of nodes, the initial number of modules and the network density are given. The network density is always 1, since we treat only complete signed networks.<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>Proportion of misplaced links is given.<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>Proportion of negative links (`<em>q<sub>n</sub></em>`) is specified. `<em>q<sub>n</sub></em>` changes depending on `<em>n</em>` and `<em>l<sub>0</sub></em>`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>Network numbers are shown.<br>In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.<br><br><strong># PARTITIONS</strong><br>All partition results are in `<em>Partition Results.tar.gz</em>`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: <em>https://github.com/arinik9/ExCC</em><br>Inside `<em>Partition Results.tar.gz</em>`:<br><br>PARTITIONS<br>|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000<br>....|__propMispl=PROP_MISPL<br>........|__propNeg=PROP_NEG<br>............|__network=NETWORK_NO<br>................|__"<em>ExCC-all</em>"<br>....................|__"<em>signed-unweighted</em>"<br><br>- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000<br>- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL<br>- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG<br>- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO<br>- The fifth hierarchy => the folders are named as follows: "<em>ExCC-all</em>"<br>The name of the partitioning method are shown. Since an exact partitioning method is used to obtain all distinct optimal solutions, it is named as "<em>ExCC-all</em>".<br>- The sixth hierarchy => the folders are named as follows: "<em>signed-unweighted</em>"<br>The type of signed networks are shown: signed and unweighted</p> <p>In the end, the partition results are located, and the file names are named as follows: <em>membership.txt</em>. Note that the first partition result number starts from zero.</p> <p> </p> <p><strong># EVALUATIONS</strong><br>Evaluation results related to our plots are in `<em>Evaluation Results.tar.gz</em>. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `<em>Evaluation</em><em> Results.tar.gz</em>`:</p> <p>- `Best-k-for-kmedoids.csv`: It contains three columns. 1) the number of solution classes via kmedoids, 2) the best Silhouette score, 3) the best clustering in terms of Silhouette score, which represents solution classes.</p> <p>- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.</p> <p>- `exec-time.csv`: It indicates the execution time in seconds.</p> <p>- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage</p> <p>- `nb-solution.csv`: It indicates the total number of solutions<br>--------------------------------------------</p> <p>Funding: this research benefited from the support of the Agorantic FR 3621, as well as the FMJH Program PGMO and from the support to this program from EDF-THALES-ORANGE-CRITEO.</p>
Figure 3: AntCo2 algorithm for graph clustering: on the left the output of the computation on a communication network; on the right the output on a regular grid
<p>Social and human developments are typical complex systems. Urban development<br> and dynamics are the perfect illustration of systems where spatial<br> emergence, self-organization and structural interaction between the system<br> and its components occur [3, 4, 5, 6]. In figure 4, we concentrate on the emergence<br> of organizational systems from geographical systems.</p>
A supervised Graph-based deep learning algorithm to detect and quantify clustered particles
<p>In this data repository, we provide the necessary data for replicating results, including both simulated and biological datasets. Additionally, the repository includes trained models to infer from these datasets.</p>
Pangenomes of multiple species for the "Cluster efficient pangenome graph construction with nf-core/pangenome" manuscript.
<p>Pangenomes of multiple species for the "Cluster efficient pangenome graph construction with nf-core/pangenome" manuscript.</p> <p>Each pangenome is represented in a FASTA format file. Each FASTA file was compressed with <em>bgzip</em> and subsequent indices were created with <em>tabix</em>.</p> <p>The name of each file specifies:</p> <ul> <li>The species,</li> <li>and the number of haplotypes.</li> </ul> <p>The built pangenomes graph are represented in GFA format. Each pangenome graph in the paper is uploaded here, too.</p>
Deep Clustering Representation for Spatially Resolved Transcriptomics Data via Multi-view Variational Graph Auto-Encoders with Consensus Clustering
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Data from: Discovering biogeographic and ecological clusters with a graph theoretic spin on factor analysis
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Supplemental Material for Paper 'Taxonomy Extraction Using Knowledge Graph Embeddings and Hierarchical Clustering'
<p>Contains input data and gold standard for the non-expressive extraction task, as well as examples of extracted taxonomies for both the non-expressive and expressive cases. Extracted taxonomies can also be found at <a href="http://labowest.ca/sdb2020/">labowest.ca</a>.</p>
Dataset for "Decomposing God Header File via Multi-View Graph Clustering"
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Dataset - Clustering Semantic Predicates in the Open Research Knowledge Graph
<p>This dataset has been created for implementing a content-based recommender system in the context of the Open Research Knowledge Graph (ORKG). The recommender system accepts research paper's title and abstracts as input and recommends existing predicates in the ORKG semantically relevant to the given paper.</p> <p> </p> <p>The paper instances in the dataset are grouped by ORKG comparisons and therefore the <strong><em>data.json</em></strong> file is more comprehensive than <strong><em>training_set.json</em></strong> and <strong><em>test_set.json.</em></strong></p> <p> </p> <p><strong><em>data.json</em></strong></p> <p>The main JSON object consists of a list of comparisons. Each comparisons object has an ID, label, list of papers and list of predicates, whereas each paper object has ID, label, DOI, research field, research problems and abstract. Each predicate object has an ID and a label. See an example instance below.</p> <pre><code class="language-json">{ "comparisons": [ { "id": "R108331", "label": "Analysis of approaches based on required elements in way of modeling", "papers": [ { "id": "R108312", "label": "Rapid knowledge work visualization for organizations", "doi": "10.1108/13673270710762747", "research_field": { "id": "R134", "label": "Computer and Systems Architecture" }, "research_problems": [ { "id": "R108294", "label": "Enterprise engineering" } ], "abstract": "Purpose \u2013 The purpose of this contribution is to motivate a new, rapid approach to modeling knowledge work in organizational settings and to introduce a software tool that demonstrates the viability of the envisioned concept.Design/methodology/approach \u2013 Based on existing modeling structures, the KnowFlow toolset that aids knowledge analysts in rapidly conducting interviews and in conducting multi\u2010perspective analysis of organizational knowledge work is introduced.Findings \u2013 This article demonstrates how rapid knowledge work visualization can be conducted largely without human modelers by developing an interview structure that allows for self\u2010service interviews. Two application scenarios illustrate the pressing need for and the potentials of rapid knowledge work visualizations in organizational settings.Research limitations/implications \u2013 The efforts necessary for traditional modeling approaches in the area of knowledge management are often prohibitive. This contribution argues that future research needs ..." }, .... ], "predicates": [ { "id": "P37126", "label": "activities, behaviours, means [for knowledge development and/or for knowledge conveyance and transformation" }, { "id": "P36081", "label": "approach name" }, .... ] }, .... ] }</code></pre> <p> </p> <p><strong><em>training_set.json </em></strong>and<strong><em> test_set.json</em></strong></p> <p>The main JSON object consists of a list of training/test instances. Each instance has an instance_id with the format (comparison_id X paper_id) and a text. The text is a concatenation of the paper's label (title) and abstract. See an example instance below.</p> <p>Note that test instances are not duplicated and do not occur in the training set. Training instances are also not duplicated, BUT training papers can be duplicated in a concatenation with different comparisons.</p> <pre><code class="language-json">{ "instances": [ { "instance_id": "R108331xR108301", "comparison_id": "R108331", "paper_id": "R108301", "text": "A notation for Knowledge-Intensive Processes Business process modeling has become essential for managing organizational knowledge artifacts. However, this is not an easy task, especially when it comes to the so-called Knowledge-Intensive Processes (KIPs). A KIP comprises activities based on acquisition, sharing, storage, and (re)use of knowledge, as well as collaboration among participants, so that the amount of value added to the organization depends on process agents' knowledge. The previously developed Knowledge Intensive Process Ontology (KIPO) structures all the concepts (and relationships among them) to make a KIP explicit. Nevertheless, KIPO does not include a graphical notation, which is crucial for KIP stakeholders to reach a common understanding about it. This paper proposes the Knowledge Intensive Process Notation (KIPN), a notation for building knowledge-intensive processes graphical models." }, ... ] }</code></pre> <p> </p> <p><strong>Dataset Statistics:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Predicates</th> <th scope="col">Research Fields</th> <th scope="col">Research Problems</th> </tr> </thead> <tbody> <tr> <td>Min/Comparison</td> <td>2</td> <td>2</td> <td>1</td> <td>0</td> </tr> <tr> <td>Max/Comparison</td> <td>202</td> <td>112</td> <td>5</td> <td>23</td> </tr> <tr> <td>Avg./Comparison</td> <td>21,54</td> <td>12,79</td> <td>1,20</td> <td>1,09</td> </tr> <tr> <td>Total</td> <td>4060</td> <td>1816</td> <td>46</td> <td>178</td> </tr> </tbody> </table> <p><strong>Dataset Splits:</strong></p> <table align="center"> <thead> <tr> <th scope="col">-</th> <th scope="col">Papers</th> <th scope="col">Comparisons</th> </tr> </thead> <tbody> <tr> <td>Training Set</td> <td>2857</td> <td>214</td> </tr> <tr> <td>Test Set</td> <td>1203</td> <td>180</td> </tr> </tbody> </table> <p> </p>
SpatialCVGAE: Spatial Domain Identification via Consensus Clustering Integrated Variational Graph Autoencoder
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Undirected scale-free graphs for cluster-BFS
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MuGNN: API Misuse Detection using Graph Neural Networks and Clustering
<div> <div>This artifact presents `MuGNN`, a novel framework for efficiently detecting API misuse in Java code. The approach leverages a `Graph Neural Network (GNN)` model to generate embeddings of Java API usage code using a custom `API Flow Graph (AFG)` representation. This representation captures execution sequences, data flow, and control flow, enabling better understanding of API usage patterns. MuGNN employs self-supervised pre-training and clustering to analyze API usage and identify potential misuse.</div> </div>
Data from: Graph analysis of cell clusters forming vascular networks
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PHD Thesis: Graph Set Data Mining - Clustering and Pattern Mining in the Context of Cheminformatics - Evaluation Data
<p>Evaluation data for the PHD thesis:</p> <p>Graph Set Data Mining<br> Clustering and Pattern Mining in the Context of Cheminformatics</p> <p>zur Erlangung des Grades eines<br> Doktors der Naturwissenschaften<br> der Technischen Universität Dortmund<br> an der Fakultät für Informatik</p> <p>von</p> <p>TIll Schäfer</p>
ScienceDex guides
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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.