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@article{chavalarias:hal-02429929,
author = {Chavalarias, David and Gaumont, Noe and Panahi, Maziyar},
doi = {10.3917/res.214.0067},
journal = {R{\'{e}}seaux},
number = {214-215},
pages = {67--107},
publisher = {La D{\'{e}}couverte},
series = {Enqu{\^{e}}ter {\`{a}} partir des traces textuelles du web},
title = {{Hostilit{\'{e}} et pros{\'{e}}lytisme des communaut{\'{e}}s politiques}},
url = {https://hal.archives-ouvertes.fr/hal-02429929},
year = {2019}
}
@inproceedings{Gaumont2014,
abstract = {La recherche de communaut{\'{e}}s chevauchantes est un enjeu important pour l'analyse des r{\'{e}}seaux complexes. Une piste souvent envisag{\'{e}}e est la recherche d'un partitionnement des ar{\^{e}}tes du graphe. L'{\'{e}}valuation de cette d{\'{e}}composition tient cependant rarement compte du fait que les communaut{\'{e}}s recherch{\'{e}}es correspondent {\`{a}} des groupes d'ar{\^{e}}tes. Nous discutons dans ce papier l'utilisation de nouveaux crit{\`{e}}res pouvant r{\'{e}}pondre {\`{a}} ce probl{\`{e}}me. Nous proposons de comparer le nombre de sommets incidents {\`{a}} un groupe d'ar{\^{e}}tes au nombre attendu dans un graphe al{\'{e}}atoire. Un optimum local de la mesure d{\'{e}}riv{\'{e}}e de ce concept peut {\^{e}}tre obtenu par un algorithme glouton. Nous pr{\'{e}}sentons les premiers r{\'{e}}sultats obtenus {\`{a}} travers une analyse de la mesure et des tests empiriques.},
author = {Gaumont, No{\'{e}} and Queyroi, Fran{\c{c}}ois},
booktitle = {ALGOTEL 2014 -- 16{\`{e}}mes Rencontres Francophones sur les Aspects Algorithmiques des T{\'{e}}l{\'{e}}communications},
language = {fr},
pages = {1--4},
title = {{Partitionnement des Liens d'un Graphe : Crit{\`{e}}res et Mesures}},
url = {https://hal.archives-ouvertes.fr/hal-00986216},
year = {2014}
}
@article{Gaumont2018,
abstract = {Background Digital spaces, and in particular social networking sites, are becoming increasingly present and influential in the functioning of our democracies. In this paper, we propose an integrated methodology for the data collection, the reconstruction, the analysis and the visualization of the development of a country's political landscape from Twitter data. Method The proposed method relies solely on the interactions between Twitter accounts and is independent of the characteristics of the shared contents such as the language of the tweets. We validate our methodology on a case study on the 2017 French presidential election (60 million Twitter exchanges between more than 2.4 million users) via two independent methods: the comparison between our automated political categorization and a human categorization based on the evaluation of a sample of 5000 profiles descriptions; the correspondence between the reconfigurations detected in the reconstructed political landscape and key political events reported in the media. This latter validation demonstrated the ability of our approach to accurately reflect the reconfigurations at play in the off-line political scene. Results We built on this reconstruction to give insights into the opinion dynamics and the reconfigurations of political communities at play during a presidential election. First, we propose a quantitative description and analysis of the political engagement of members of political communities. Second, we analyze the impact of political communities on information diffusion and in particular on their role in the fake news phenomena. We measure a differential echo chamber effect on the different types of political news (fake news, debunks, standard news) caused by the community structure and emphasize the importance of addressing the meso-structures of political networks in understanding the fake news phenomena. Conclusions Giving access to an intermediate level, between sociological surveys in the field and large statistical studies (such as those conducted by national or international organizations) we demonstrate that social networks data make it possible to qualify and quantify the activity of political communities in a multi-polar political environment; as well as their temporal evolution and reconfiguration, their structure, their alliance strategies and their semantic particularities during a presidential campaign through the analysis of their digital traces. We conclude this paper with a comment on the political and ethical implications of the use of social networks data in politics. We stress the importance of developing social macroscopes that will enable citizens to better understand how they collectively make society and propose as example the “Politoscope”, a macroscope that delivers some of our results in an interactive way.},
author = {Gaumont, No{\'{e}} and Panahi, Maziyar and Chavalarias, David},
doi = {10.1371/journal.pone.0201879},
issn = {19326203},
journal = {PLoS ONE},
number = {9},
title = {{Methods for the reconstruction of the socio-semantic dynamics of political activist Twitter networks: Application to the 2017 French Presidential elections}},
url = {https://hal.archives-ouvertes.fr/hal-01575456v3},
volume = {13},
year = {2018}
}
@inproceedings{Gaumont2016,
abstract = {A link stream is a collection of triplets (t, u, v) indicating that an interaction occurred between u and v at time t. Link streams model many real-world situations like email exchanges between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. In this paper, we generalize the existing notions of intra-community density and inter-community density. We focus on emails exchanges in the Debian mailing-list and show that threads of emails, like communities in graphs, are dense subsets loosely connected from a link stream perspective.},
author = {Gaumont, No{\'{e}} and Viard, Tiphaine and Fournier-S'niehotta, Rapha{\"{e}}l and Wang, Qinna and Latapy, Matthieu},
booktitle = {Studies in Computational Intelligence},
doi = {10.1007/978-3-319-30569-1_8},
isbn = {9783319305684},
issn = {1860949X},
pages = {107--118},
title = {{Analysis of the temporal and structural features of threads in a mailing-list}},
volume = {644},
year = {2016}
}
@inproceedings{Gaumont2019,
author = {Gaumont, No{\'{e}} and Panahi, Maziyar and Chavalarias, David},
booktitle = {15th Iteration (2019): Macroscopes for Tracking the Flow of Resources},
editor = {Katy, B{\"{o}}rner and Lisel, Record},
title = {{Politoscope}},
year = {2019}
}
@inproceedings{Gaumont2016b,
address = {Toulouse},
author = {Gaumont, No{\'{e}}},
booktitle = {Workshop Outils d'analyse de la dynamique temporelle dans les r{\'{e}}seaux},
title = {{Tools to study link streams}},
url = {https://xsys.fr/wp-content/uploads/2016/09/journée-du-14-decembre.pdf},
year = {2016}
}
@inproceedings{Gaumont2017a,
author = {Gaumont, No{\'{e}} and Panahi, Maziyar and Chavalarias, David},
booktitle = {Colloque international sur L'{\'{e}}lection pr{\'{e}}sidentielle de 2017 et ses primaires : enjeux de communication politique},
title = {{{\'{E}}tude de la campagne Twitter “Ali Jupp{\'{e}}”}},
url = {http://www.iscc.cnrs.fr/spip.php?article2282},
year = {2017}
}
@article{Gaumont2016a,
abstract = {A link stream is a set of quadruplets (b, e, u, v) meaning that a link exists between u and v from time b to time e. Link streams model many real-world situations like contacts between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. We argue that the density is a valuable notion for understanding and characterizing links streams. We propose a method to capture specific groups of links that are structurally and temporally densely connected and show that they are meaningful for the description of link streams. To find such groups, we use classical graph community detection algorithms, and we assess obtained groups. We apply our method to several real-world contact traces (captured by sensors) and demonstrate the relevance of the obtained structures.},
author = {Gaumont, No{\'{e}} and Magnien, Cl{\'{e}}mence and Latapy, Matthieu},
doi = {10.1007/s13278-016-0396-z},
issn = {18695469},
journal = {Social Network Analysis and Mining},
keywords = {Dense subgraphs,Density,Link stream,Temporal network,face-to-face interaction},
number = {1},
pages = {87},
title = {{Finding remarkably dense sequences of contacts in link streams}},
url = {https://hal.archives-ouvertes.fr/hal-01390043},
volume = {6},
year = {2016}
}
@inproceedings{Danisch2019,
abstract = {We propose an overlapping community detection algorithm following a “from local to global approach”: our algorithm finds local communities one by one by repetitively optimizing a quality function that measures the quality of a community. Then, as some extracted local communities can be very similar to each-other, a cleaning procedure is applied to obtain the global overlapping community structure. Our algorithm depends on three modules: (i) a quality function, (ii) an optimization heuristic and (iii) a cleaning procedure. Various such modules can be independently plugged in. We show that, using default modules, our algorithm improves over a state-of-the-art method on some real-world graphs with ground truth communities. In the future we would like to study which combination of modules performs best in practice and make our code parallel.},
author = {Danisch, Maximilien and Gaumont, No{\'{e}} and Guillaume, Jean Loup},
booktitle = {16th Cologne-Twente Workshop on Graphs and Combinatorial Optimization, CTW 2018 - Proceedings of the Workshop},
pages = {156--159},
title = {{A modular overlapping community detection algorithm: Investigating the “from local to global” approach}},
url = {https://papers-gamma.link/paper/33/A%2520Modular%2520Overlapping%25%0A20Community%2520Detection%2520Algorithm:%2520Investigating%2520the%2520%25E2%2580%259CFrom%2520Local%25%0A20to%2520Global%25E2%2580%259D%2520Approach},
year = {2018}
}
@inproceedings{Gaumont2015-density,
author = {Gaumont, No{\'{e}} and Magnien, Cl{\'{e}}mence and Latapy, Matthieu},
booktitle = {Workshop e-Young Researchers Network in Complex Systems},
title = {{Bringing density to link streams reveals meaningful groups in contact traces}},
url = {https://cs-dc-15.org/e-tracks/global/#yr},
year = {2015}
}
@inproceedings{Viard2016,
author = {Viard, Tiphaine and Gaumont, No{\'{e}}},
booktitle = {Workshop Dynamics On and Of networks},
title = {{LinkStreamViz: a drawing tool for link stream}},
url = {https://project.inria.fr/netspringlyon/%0A3-workshops-on-network-sciences/workshop-on-processes-on-and-of-networks/},
year = {2016}
}
@inproceedings{Gaumont2017,
author = {Gaumont, No{\'{e}} and Panahi, Maziyar and Chavalarias, David},
booktitle = {Conference on complex systems},
title = {{Evolution of communities on twitter during the 2017 French presidential election}},
url = {https://easychair.org/smart-program/CCS'17/2017-09-18.html#talk:47444},
year = {2017}
}
@article{Chavalarias2019,
author = {Chavalarias, David and Gaumont, No{\'{e}} and Panahi, Maziyar},
doi = {10.3917/res.214.0067},
issn = {07517971},
journal = {Reseaux},
number = {2},
pages = {67--107},
title = {{Hostili{\'{e}} et pros{\'{e}}lytisme des communaut{\'{e}}s politiques: Le militantisme politique {\`{a}} l'{\`{e}}re des r{\'{e}}seaux sociaux}},
url = {https://hal.archives-ouvertes.fr/hal-02429929},
volume = {214-215},
year = {2019}
}
@incollection{Gaumont2015a,
address = {New York, New York, USA},
author = {Gaumont, No{\'{e}} and Queyroi, Fran{\c{c}}ois and Magnien, Cl{\'{e}}mence and Latapy, Matthieu},
booktitle = {6th Workshop on Complex Networks CompleNet},
doi = {10.1007/978-3-319-16112-9\_6},
pages = {57--64},
publisher = {Springer International Publishing},
series = {Studies in Computational Intelligence},
title = {{Expected Nodes: a quality function for the detection of link communities}},
url = {https://hal.sorbonne-universite.fr/hal-01196796},
year = {2015}
}
@article{Chavalarias,
address = {Paris, France},
author = {Chavalarias, David and Panahi, Maziyar and Gaumont, No{\'{e}}},
editor = {{Cit{\'{e}} des Sciences et de l'Industrie}},
journal = {Cit{\'{e}} des Sciences et de l'Industrie},
number = {4 Avril 2017 - 7 janvier 2018},
title = {{Politoscope}},
url = {http://scimaps.org},
volume = {Terra-data},
year = {2018}
}
@inproceedings{Gaumont2017b,
author = {Gaumont, No{\'{e}}},
booktitle = {24e journ{\'{e}}es th{\'{e}}matique de Rochebrune},
title = {{Utilisation de flots de liens pour {\'{e}}tudier les interactions temporelles}},
year = {2017}
}
@article{Gaumont2015,
abstract = {Many studies use community detection algorithms in order to understand complex networks. Most papers study node communities, i.e. groups of nodes, which may or may not overlap. A widely used measure to evaluate the quality of a community structure is the modularity. However, sometimes it is also relevant to study link partitions rather than node partitions. In order to evaluate a link partition, we propose a new quality function: Expected Nodes. Our function is based on the same inspiration as the modularity and compares, for a given link group, the number of incident nodes to the expected one. In this short note, we discuss the advantages and drawbacks of our quality function compared to other ones on synthetics graphs. We show that Expected Nodes is able to pass some fundamental sanity criteria and is the one that best identifies the most relevant partition in a more realistic context.},
author = {Gaumont, No{\'{e}} and Queyroi, Fran{\c{c}}ois and Magnien, Cl{\'{e}}mence and Latapy, Matthieu},
doi = {10.1007/978-3-319-16112-9_6},
issn = {1860949X},
journal = {Studies in Computational Intelligence},
keywords = {Community detection,Complex networks,Link partition,Quality measure},
pages = {57--64},
title = {{Expected nodes: A quality function for the detection of link communities}},
volume = {597},
year = {2015}
}
@article{gaumont:hal-01575456,
annote = {La version fran{\c{c}}aise est disponible en V1 de cette archive.},
author = {Gaumont, Noe and Panahi, Maziyar and Chavalarias, David},
doi = {10.1371/journal.pone.0201879},
journal = {PLoS ONE},
keywords = {Clustering Algorithms ; Community detection in gra},
number = {9},
publisher = {Public Library of Science},
title = {{Methods for the reconstruction of the socio-semantic dynamics of political activist Twitter networks}},
url = {https://hal.archives-ouvertes.fr/hal-01575456},
volume = {13},
year = {2018}
}
@inproceedings{gaumont:hal-01305118,
address = {Bayonne, France},
author = {Gaumont, No{\'{e}}},
booktitle = {ALGOTEL 2016 - 18{\`{e}}mes Rencontres Francophones sur les Aspects Algorithmiques des T{\'{e}}l{\'{e}}communications},
keywords = {flot de liens,groupes denses,r{{\'{e}}}seaux de cont},
month = {may},
series = {ALGOTEL 2016 - 18{{\`{e}}}mes Rencontres Francophones sur les Aspects Algorithmiques des T{{\'{e}}}l{{\'{e}}}communications},
title = {{Trouver des s{\'{e}}quences de contacts pertinentes dans un flot de liens}},
url = {https://hal.archives-ouvertes.fr/hal-01305118},
year = {2016}
}
@incollection{Gaumont2016c,
address = {Dijon, France},
author = {Gaumont, No{\'{e}} and Viard, Tiphaine and Fournier-S'niehotta, Rapha{\"{e}}l and Wang, Qinna and Latapy, Matthieu},
booktitle = {7th Workshop on Complex Networks CompleNet},
doi = {10.1007/978-3-319-30569-1\_8},
pages = {107--118},
publisher = {Springer},
series = {Studies in Computational Intelligence},
title = {{Analysis of the Temporal and Structural Features of Threads in a Mailing-List}},
url = {https://hal.archives-ouvertes.fr/hal-01345821},
year = {2016}
}