Articles by Vincent Labatut

These were designed for three purposes: Generate a variety of plots and statistics based on some raw data describing the voting activity of a population. Extract so-called vote networks from these data. Perform various analyses on these networks, in particular: estimate good partitions of the network, according to different measures. Our tool was applied to data representing the activity of the members of the European Parliament (MEPs) during the 7th term (from June 2009 to June 2014), as described in [MFL’15a, MFL’15b]. The raw data describing this activity were first retrieved from the VoteWatch website. However, these data were incomplete, so we later switched to another source: the It’s Your Parliament website. There were also some minor issues with these data, which we had to correct: some MEPs were represented twice, some profiles were incomplete, the policy domains were not defined for all vote texts, etc. These cleaned data are available on Zenodo here and there. URL: https://github.com/CompNet/NetVotes Production date: 2014–2018 Related publications: Nejat Arınık, Rosa Figueiredo et Vincent Labatut. « Signed Graph Analysis for theInterpretation of Voting Behavior ». In : International Conference on Knowledge Technologiesand Data-driven Business (i-KNOW) – International Workshop on Social NetworkAnalysis and Digital Humanities (SnanDig). T. 2025. CEUR Workshop Proceedings.Graz, Plus d'infos

These scripts were designed to compute several variants of the Straightness (aka. Directness and probably other names): the ratio of the Euclidean to the graph distance. It is a measure designed to study spatial graphs, i.e. graphs embedded in a Euclidean space (nodes have spatial positions, links have spatial length, etc.). First, this toolbox can process the Straightness using the traditional approach, i.e. considering only paths connecting two nodes. It can process the Straightness between two specific nodes, or the Straightness averaged over certain pairs of nodes in the graph (possibly all of them). Second, this toolbox can also compute the average Straightness through a continuous approach (by opposition to the discrete traditional approach), and incidentally this is the point of the below article. URL: https://github.com/CompNet/SpatialMeasures Production date: 2016 Related publication:  Vincent Labatut. « Continuous Average Straightness in Spatial Graphs ». In : Journalof Complex Networks 6(2):269-296 (2018). DOI: 10.1093/comnet/cnx033. ⟨hal-01571212⟩

These scripts were designed for two purposes: Process the opinion centrality, a new centrality measure for multiplex networks. Compare it to other existing multiplex centrality measures. Our scripts were applied to a collection of multiplex networks obtained from public sources and provided in our GitHub project. The tool itself, the data and the experimental results are all described in the below article. URL: https://github.com/CompNet/MultiplexCentrality Production date: 2015–2016 Related publication:  Alexandre Reiffers et Vincent Labatut. « Opinion-based centrality in multiplexnetworks : A convex optimization approach ». In : Network Science 5(2):213-234 (2017). DOI: 10.1017/nws.2017.7. ⟨hal-01486629⟩  (cite this article if you use the software)

These scripts were designed to generate various types of spatial graphs, and compute certain topological properties. More precisely, the goal here is to study so-called orb-web networks, which mimic typical spider webs, with a focus on the straightness measure.

These scripts are meant to extract certain features from raw Twitter data describing Twitter users (tweets, profile info, as well as external data). Once the features are extracted, various forms of SVMs are trained, and logistic regressions are performed, to classify and rank the users. These operations are conducted on different subgroups of features. The details of the process are given in the below publications. The scripts were applied to the classification/ranking of Twitter users in terms of offline influence, based on the RepLab 2014 dataset.

This software aims at studying social capitalists, which are a specific type of users of social networks services such as Twitter. The tool is generic, so it can actually be applied to completely different systems, as long as they can be represented as directed networks (i.e. digraphs). We applied our tool to Twitter in several research papers, listed below. Our work was also mentioned on the blog of the MIT Technology review. URL: https://github.com/CompNet/Orleans Release date: 2013–2014 Related publications: Nicolas Dugué, Vincent Labatut et Anthony Perez. « A Community Role ApproachTo Assess Social Capitalists Visibility in the Twitter Network ». In : Social NetworkAnalysis and Mining (SNAM) 5:26 (2015). DOI: 10.1007/s13278-015-0266-0.⟨hal-01163741⟩ Nicolas Dugué, Vincent Labatut et Anthony Perez. « Identifying the CommunityRoles of Social Capitalists in the Twitter Network ». In : IEEE/ACM InternationalConference on Advances in Social Network Analysis and Mining (ASONAM). Beijing,CN : IEEE Publishing, 2014, p. 371-374. DOI: 10.1109/ASONAM.2014.6921612.⟨hal-01011910⟩ (cite this publication if you use this software) Nicolas Dugué, Vincent Labatut et Anthony Perez. « Identification de rôles communautairesdans des réseaux orientés, appliquée à Twitter ». In : 14ème ConférenceExtraction et Gestion des Connaissances (EGC). Rennes, FR, 2014, p. 125-130. EGC ⟨hal-00918175⟩ Nicolas Dugué, Vincent Labatut et Plus d'infos

These scripts implement several measures allowing to compare two community structures, i.e. two partitions of the node set of a given graph. They are based on popular measures defined in the field of cluster analysis, namely: The variants implemented here account for the network structure, an essential aspect of community structure which is otherwise completely ignored in standard measures.