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ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Dataset . 2020
License: CC BY
Data sources: Datacite
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Graphs and Attributes used for the attribute-structure correlation pattern mining

Authors: Silva, Arlei; Meira, Jr. Wagner; Zaki, Mohammed J.;

Graphs and Attributes used for the attribute-structure correlation pattern mining

Abstract

## SCPM: An implementation of an algorithm for structural correlation pattern mining. 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. **Relevant Publications** * 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). * 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). * Arlei Silva. Structural correlation pattern mining for large graphs. M.Sc Thesis, Computer Science Department, Universidade Federal de Minas Gerais, 2011. * Arlei Silva, Wagner Meira Jr. Structural correlation pattern mining for large graphs. Thesis and Dissertation Contest of the Brazilian Computer Society (CTD'12). ## HOW TO cd to trunk and run make see README in trunk ## Datasets: ### Description: #### ATTRIBUTE FILE: Format: Lists the attributes of each vertex from the graph. <VERTEX_ID>,<ATTRIBUTE_ID>,<ATTRIBUTE_ID>...,<ATTRIBUTE_ID> Example: 1,A,C 2,A 3,A,C,D 4,A,D 5,A,E 6,A,B,C 7,A,B,E 8,A,B 9,A,B 10,A,B,D 11,A,B #### GRAPH FILE: 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. <VERTEX_ID>,<NEIGHBOR_ID>,<NEIGHBOR_ID>...,<NEIGHBOR_ID> Example: 1,4 2,3 3,2,4,5,6,7 4,1,3,5,6 5,3,4,6 6,3,4,5,7,8,9,10 7,3,6,8,11 8,6,7,9,10,11 9,6,8,10,11 10,6,8,9,11 11,7,8,9,10 ### REAL DATASETS Lastfm: attributes: attrLastFm.csv.tar.bz2 network: graphLastFm.csv.tar.gz DBLP: attributes: newAttrDBLP.csv.tar.bz2 network: newGraphDBLP.csv.tar.bz2 CITESEER: attributes: attrCiteseer.csv.tar.bz2 network: graphCiteseer.csv.tar.bz2

{"references": ["Arlei Silva, Mohammed J. Zaki, and Wagner Meira Jr. Mining attribute-structure correlated patterns in large attributed graphs. PVLDB, 5(5):466\u2013477, 2012."]}

Keywords

graph mining, attribute-structure correlation pattern mining, social networks, attributed graphs

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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