DOI: 10.5281/zenodo.6815510
title: NetVotes ENIC Dataset

description: Description. The NetVote dataset contains the outputs of the NetVote program when applied to voting data coming from VoteWatch (http://www.votewatch.eu/). 

These results were used in the following conference papers:



I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the European Parliament,” in 2nd European Network Intelligence Conference, 2015, pp. 122–129. ⟨hal-01176090⟩ DOI: 10.1109/ENIC.2015.25

I. Mendonça, R. Figueiredo, V. Labatut, and P. Michelon, “Informative Value of Negative Links for Graph Partitioning, with an application to European Parliament Votes,” in 6ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques, 2015, p. 12p. ⟨hal-02055158⟩


Source code. The NetVote source code is available on GitHub: https://github.com/CompNet/NetVotes. 

Citation. If you use our dataset or tool, please cite article [1] above.

@InProceedings{Mendonca2015,  author               = {Mendonça, Israel and Figueiredo, Rosa and Labatut, Vincent and Michelon, Philippe},  title                = {Relevance of Negative Links in Graph Partitioning: A Case Study Using Votes From the {E}uropean {P}arliament},  booktitle            = {2\textsuperscript{nd} European Network Intelligence Conference ({ENIC})},  year                 = {2015},  pages                = {122-129},  address              = {Karlskrona, SE},  publisher            = {IEEE Publishing},  doi                  = {10.1109/ENIC.2015.25},}-------------------------

Details. This archive contains the following folders:



`votewatch_data`: the raw data extracted from the VoteWatch website.



 `VoteWatch Europe European Parliament, Council of the EU.csv`: list of the documents voted during the considered term, with some details such as the date and topic.

`votes_by_document`: this folder contains a collection of CSV files, each one describing the outcome of the vote session relatively to one specific document.

`intermediate_files`: this folder contains several CSV files:



`allvotes.csv`: concatenation of all vote outcomes for all documents and all MEPS. Can be considered as a compact representation of the data contained in the folder `votes_by_document`.

`loyalty.csv`: same thing than allvotes.csv, but for the loyalty (i.e. whether or not the MEP voted like the majority of the MEPs in his political group).

`MPs.csv`: list of the MEPs having voted at least once in the considered term, with their details.

`policies.csv`: list of the topics considered during the term.

`qtd_docs.csv`: list of the topics with the corresponding number of documents.





`parallel_ils_results`: contains the raw results of the ILS tool. This is an external algorithm able to estimate the optimal partition of the network nodes in terms of structural balance. It was applied to all the networks extracted by our scripts (from the VoteWatch data), and the produced files were placed here for postprocessing. Each subfolder corresponds to one of the topic-year pair.

`output_files`: contains the file produced by our scripts.



`agreement`: histograms representing the distributions of agreement and rebellion indices. Each subfolder corresponds to a specific topic.

`community_algorithms_csv`: Performances obtained by the partitioning algorithms (for both community detection and correlation clustering). Each subfolder corresponds to a specific topic.

`xxxx_cluster_information.csv`: table containing several variants of the imbalance measure, for the considered algorithms.

`community_algorithms_results`: Comparison of the partitions detected by the various algorithms considered, and distribution of the cluster/community sizes. Each subfolder corresponds to a specific topic.

`xxxx_cluster_comparison.csv`: table comparing the partitions detected by the community detection algorithms, in terms of Rand index and other measures.

`xxxx_ils_cluster_comparison.csv`: like `xxxx_cluster_comparison.csv`, except we compare the partition of community detection algorithms with that of the ILS.

`xxxx_yyyy_distribution.pdf`: histogram of the community (or cluster) sizes detected by algorithm `yyyy`.

`graphs`: the networks extracted from the vote data. Each subfolder corresponds to a specific topic.

`xxxx_complete_graph.graphml`: network at the Graphml format, with all the information: nodes, edges, nodal attributes (including communities), weights, etc.

`xxxx_edges_Gephi.csv`: only the links, with their weights (i.e. vote similarity).

`xxxx_graph.g`: network at the g format (for ILS).

`xxxx_net_measures.csv`: table containing some stats on the network (number of links, etc.).

`xxxx_nodes_Gephi.csv`: list of nodes (i.e. MEPs), with details.

`plots`: synthesis plots from the paper.




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License. These data are shared under a Creative Commons 0 license. 

Contact. Vincent Labatut <vincent.labatut@univ-avignon.fr> & Rosa Figueiredo <rosa.figueiredo@univ-avignon.fr>