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7 results for “graph mining”
Graphs and Attributes used for the attribute-structure correlation pattern mining
<p>## SCPM: An implementation of an algorithm for structural correlation pattern mining.</p> <p>The structural correlation measures how a set of attributes induces dense subgraphs in an attributed graph. A structural correlation pattern is a dense subgraph induced by a particular attribute set. Structural correlation pattern mining is useful to analyze how different attribute sets are correlated to dense subgraphs in several real-life attributed graphs.</p> <p>**Relevant Publications**</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Structural correlation pattern mining for large graphs. In Proceedings of the Eighth Workshop on Mining and Learning with Graphs (MLG '10).</p> <p>* Arlei Silva, Wagner Meira, Jr., and Mohammed J. Zaki. Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. In Proceedings of the VLDB Endowment (PVLDB '12).</p> <p>* Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011.</p> <p>* Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12).</p> <p><br> ## HOW TO</p> <p>cd to trunk and run make<br> see README in trunk</p> <p><br> ## Datasets:</p> <p>### Description:</p> <p>#### ATTRIBUTE FILE:</p> <p>Format: Lists the attributes of each vertex from the graph.</p> <p> <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID></p> <p> Example: <br> 1,A,C <br> 2,A <br> 3,A,C,D <br> 4,A,D <br> 5,A,E <br> 6,A,B,C <br> 7,A,B,E <br> 8,A,B <br> 9,A,B <br> 10,A,B,D <br> 11,A,B</p> <p>#### GRAPH FILE:</p> <p>Format: Lists the neighbors of each vertex from the graph (adjacency list). Although the graph is undirected, each edge must be included in both directions.</p> <p> <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> <br> <br> Example: <br> 1,4 <br> 2,3 <br> 3,2,4,5,6,7 <br> 4,1,3,5,6 <br> 5,3,4,6 <br> 6,3,4,5,7,8,9,10 <br> 7,3,6,8,11 <br> 8,6,7,9,10,11 <br> 9,6,8,10,11 <br> 10,6,8,9,11 <br> 11,7,8,9,10</p> <p>### REAL DATASETS</p> <p>Lastfm:</p> <p>attributes: attrLastFm.csv.tar.bz2</p> <p>network: graphLastFm.csv.tar.gz</p> <p>DBLP:</p> <p>attributes: newAttrDBLP.csv.tar.bz2</p> <p>network: newGraphDBLP.csv.tar.bz2</p> <p>CITESEER:</p> <p>attributes: attrCiteseer.csv.tar.bz2</p> <p>network: graphCiteseer.csv.tar.bz2</p>
Knowledge Graph: tyrolean mining documents 15th and 16th century
<p>The dataset contains a Knowledge Graph (.nq file) of two historical mining documents: “Verleihbuch der Rattenberger Bergrichter” ( Hs. 37, 1460-1463) and “Schwazer Berglehenbuch” (Hs. 1587, approx. 1515) stored by the Tyrolean Regional Archive, Innsbruck (Austria). The user of the KG may explore the montanistic network and relations between people, claims and mines in the late medieval Tyrol. The core regions concern the districts Schwaz and Kufstein (Tyrol, Austria).</p> <p>The ontology used to represent the claims is CIDOC CRM, an ISO certified ontology for Cultural Heritage documentation. Supported by the Karma tool the KG is generated as RDF (Resource Description Framework). The generated RDF data is imported into a Triplestore, in this case GraphDB, and then displayed visually. This puts the data from the early mining texts into a semantically structured context and makes the mutual relationships between people, places and mines visible.</p> <p>Both documents and the Knowledge Graph were processed and generated by the research team of the project “Text Mining Medieval Mining Texts”. The research project (2019-2022) was carried out at the university of Innsbruck and funded by go!digital next generation programme of the Austrian Academy of Sciences.</p> <p>Citeable Transcripts of the historical documents are online available:<br> Hs. 37 DOI: 10.5281/zenodo.6274562<br> Hs. 1587 DOI: 10.5281/zenodo.6274928</p>
MMiKG: A Knowledge Graph-based Platform for Path Mining of Microbiota-Mental Diseases Interactions
<p><strong>The original datasets released in MMiKG, containing relevant information like PMID of labels and relations (triples).</strong></p> <p><strong>Background:</strong></p> <p>Gut microbiota has been demonstrated to be crucial in gut-brain axis. In this research, knowledge graphs was leveraged to aggregate and assimilate relevant information on the microbiome-gut-brain axis and its intricate relationships with mental diseases and formed a knowledge graph platform named MMiKG</p> <p> ► <strong>Advantages of MMiKG:</strong></p> <ul> <li> Assist users in semantic search and visualization operations</li> <li> Make the scattered knowledge machine-readable and interpretable</li> <li> Boost users’ confidence in the accuracy of the information</li> <li> Support better decision-making</li> </ul> <p> ► <strong>What users can do with MMiKG:</strong></p> <ul> <li>Integrate diverse resources</li> <li>Infer potential associations between gut microbiota and mental diseases</li> </ul> <p><strong>Tools:</strong></p> <p>MMiKG contains '770' entities and '1,257' triples among them. These items cover '20' common mental illnesses, '270' types of gut microbes, and '480' distinct intermediates.</p> <p> ► <strong>MMiKG's original data includes two folders:</strong></p> <p> ♦ The labels folder:</p> <ul> <li>Present information of each entity’s 'Id', 'Name', 'Degree', 'Type'</li> <li>'Disease.csv', 'Intermediate.csv', 'Microbiota.csv</li> </ul> <p> ♦ The relationships folder: </p> <ul> <li>Present information of 'SourceId', 'TargetId', 'Number', 'Reference PMID'</li> <li>'Disease.csv', 'Intermediate.csv', 'Microbiota.csv' </li> </ul>
Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis - Supplementary Material
<p>Supplementary Material for paper: "Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis", submitted to BPMDS 2024</p>
A Directed Acyclic Graph of causal relations between diseases mined from the literature
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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>
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>
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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.