Nejat Arinik RSS Feed
https://figshare.com/authors/Nejat_Arinik/4762398
RSS feed for Figshare Profile Nejat Arinik<![CDATA[Learning optimal solution characteristics of the Correlation Clustering problem]]>https://figshare.com/articles/dataset/Learning_optimal_solution_characteristics_of_the_Correlation_Clustering_problem/19350284
https://figshare.com/articles/dataset/Learning_optimal_solution_characteristics_of_the_Correlation_Clustering_problem/19350284
Mon, 14 Mar 2022 08:14:08 GMT<![CDATA[Characterizing measures for the assessment of cluster analysis and community detection]]>https://figshare.com/articles/dataset/Characterizing_measures_for_the_assessment_of_cluster_analysis_and_community_detection/13109813
https://figshare.com/articles/dataset/Characterizing_measures_for_the_assessment_of_cluster_analysis_and_community_detection/13109813
Mon, 29 Nov 2021 11:22:33 GMT<![CDATA[Multiplicity in the Partitioning of Signed Graphs]]>https://figshare.com/articles/thesis/Multiplicity_in_the_Partitioning_of_Signed_Graphs/14551113
https://figshare.com/articles/thesis/Multiplicity_in_the_Partitioning_of_Signed_Graphs/14551113
Sun, 28 Nov 2021 22:23:52 GMT<![CDATA[Multiple partitioning of multiplex signed networks: Application to European parliament votes]]>https://figshare.com/articles/dataset/Multiple_partitioning_of_multiplex_signed_networks_Application_to_European_parliament_votes/17087435
https://figshare.com/articles/dataset/Multiple_partitioning_of_multiplex_signed_networks_Application_to_European_parliament_votes/17087435
Sat, 27 Nov 2021 19:55:27 GMT<![CDATA[NetVotes 2017 - iKnow’17]]>https://figshare.com/articles/dataset/NetVotes_2017_-_iKnow_17/5785833
https://figshare.com/articles/dataset/NetVotes_2017_-_iKnow_17/5785833
Sat, 27 Nov 2021 18:58:34 GMT<![CDATA[Efficient Enumeration of Correlation Clustering Optimal Solution Space]]>https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/15043911
https://figshare.com/articles/dataset/Efficient_Enumeration_of_Correlation_Clustering_Optimal_Solution_Space/15043911
Sat, 11 Sep 2021 10:08:25 GMT<![CDATA[Space of optimal solutions of the Correlation Clustering problem on Complete Signed Graphs]]>https://figshare.com/articles/dataset/Space_of_optimal_solutions_of_the_Correlation_Clustering_problem/8233340
https://figshare.com/articles/dataset/Space_of_optimal_solutions_of_the_Correlation_Clustering_problem/8233340
the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000 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.- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL Proportion of misplaced links is given.- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG Proportion of negative links (`qn`) is specified. `qn` changes depending on `n` and `l0`. Since only complete signed networks are studied, this parameter is automatically computed from the other input parameters.- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO Network numbers are shown.In the end, thre are three file formats describing the same network content: GraphML (.graphml), Pajek NET (.net) or .G format.# PARTITIONSAll partition results are in `Partition Results.tar.gz`. Note that all optimal partitions of a signed network are obtained through an exact partitioning method. The code source is accessible here: https://github.com/arinik9/ExCCInside `Partition Results.tar.gz`:PARTITIONS|__n=NB-NODE_l0=INIT_NB_MODULE_dens=1.0000 ....|__propMispl=PROP_MISPL ........|__propNeg=PROP_NEG ............|__network=NETWORK_NO ................|__"ExCC-all" ....................|__"signed-unweighted"- The first hierarchy => the folders are named as follows: n=NB-NODE_l0=INIT-NB-MODULE_dens=1.0000- The second hierarchy => the folders are named as follows: propMispl=PROP_MISPL- The third hierarchy => the folders are named as follows: propNeg=PROP_NEG- The fourth hierarchy => the folders are named as follows: network=NETWORK_NO- The fifth hierarchy => the folders are named as follows: "ExCC-all" 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 "ExCC-all".- The sixth hierarchy => the folders are named as follows: "signed-unweighted" The type of signed networks are shown: signed and unweightedIn the end, the partition results are located, and the file names are named as follows: membership.txt. Note that the first partition result number starts from zero.# EVALUATIONSEvaluation results related to our plots are in `Evaluation Results.tar.gz. Note that the hierarchy of this folder is the same as that of 'Partitions'. Inside `Evaluation Results.tar.gz`:- `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.- `class-core-part-size-tresh=1.00.csv`. It indicates the proportion of core part size for each solution class.- `exec-time.csv`: It indicates the execution time in seconds.- `imbalance.csv`: It contains the information of imbalance as 1) count and 2) percentage - `nb-solution.csv`: It indicates the total number of solutions]]>Thu, 24 Sep 2020 12:17:28 GMT