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You searched for +publisher:"Université Catholique de Louvain" +contributor:("Lambiotte, Renaud"). Showing records 1 – 3 of 3 total matches.

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Université Catholique de Louvain

1. Dahlqvist, Carl-Henrik. Essays in econophysics and applied econometrics : modeling complexity in finance.

Degree: 2018, Université Catholique de Louvain

The thesis investigates the question of causal relationships identification and characterization in the field of finance and econometrics. Each chapter contributes to the literature both methodologically and empirically. The first chapter explores the ability of transfer entropy and Granger based causality measures to identify causal relationships between financial series. In the second chapter, the environment around agents exchanging information is taken into account to avoid the issue of indirect links. The last two chapters are devoted to the development of multi-channel causality measures. As regards the empirical part, most of the emphasis is put on the characterization of financial networks and the link between their topology and the risk associated with the system or individual financial institutions. The last chapter rather considers the information transmission between countries and the role of the commodity markets as an additional channel.

(ECGE - Sciences économiques et de gestion)  – UCL, 2018

Advisors/Committee Members: UCL - SSH/IMMAQ/LFIN - Louvain Finance, UCL - Louvain School of Management, Gnabo, Jean-Yves, Béreau, Sophie, Castiaux, Annick, Saerens, Marco, Lambiotte, Renaud, Linden, Isabelle.

Subjects/Keywords: Causality; Transfer entropy; Granger causality; Multichannel causality; Finance; Systemic risk; Financial Network

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Dahlqvist, C. (2018). Essays in econophysics and applied econometrics : modeling complexity in finance. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/213323

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Dahlqvist, Carl-Henrik. “Essays in econophysics and applied econometrics : modeling complexity in finance.” 2018. Thesis, Université Catholique de Louvain. Accessed May 21, 2019. http://hdl.handle.net/2078.1/213323.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Dahlqvist, Carl-Henrik. “Essays in econophysics and applied econometrics : modeling complexity in finance.” 2018. Web. 21 May 2019.

Vancouver:

Dahlqvist C. Essays in econophysics and applied econometrics : modeling complexity in finance. [Internet] [Thesis]. Université Catholique de Louvain; 2018. [cited 2019 May 21]. Available from: http://hdl.handle.net/2078.1/213323.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dahlqvist C. Essays in econophysics and applied econometrics : modeling complexity in finance. [Thesis]. Université Catholique de Louvain; 2018. Available from: http://hdl.handle.net/2078.1/213323

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université Catholique de Louvain

2. Deville, Pierre. Understanding social dynamics through big data.

Degree: 2015, Université Catholique de Louvain

Data are everywhere. They pervade our world. From e-mails we send, online status we post or friends we call, to credit cards we swipe or papers we cite, most of our everyday actions leave digital traces. As our ability and capacity to measure natural and social phenomena is rapidly increasing at an unprecedented scale, we witness an exponential growth of all these digital traces. This growing digital information is what we call Big Data; Data that we generate and acquire far more rapidly than the rate at which we process, analyse and exploit it. In science, the ability to collect and analyse massive amounts of data traces have fuelled numerous advances and unambiguously transformed many research fields. But nowhere are these advances more important than in the study of social systems. Indeed, the flood of data capturing activities of individuals enables an entirely new scientific approach for social analysis, which this thesis aims at illustrating. More particularly, our contributions evolve around three different yet intrinsically related aspects of social dynamics: human mobility, social interactions and success. In the first part of this work, we focus on mobile phone data. We first demonstrate that these large-scale social data can provide reliable and dynamical estimates of population densities over large geographical extents, offering concrete solutions to population mapping issues in low-income countries. We then show how these data can also reveal remarkable and unexpected social structures over entire countries, as well as help us uncover universal relationships between human mobility and social interactions, fuelling applications on epidemic spreading or traffic forecasting. In the second part, we investigate the social mechanisms of success through the analysis of large-scale publication data. Publication data are a valuable source of individual information as mobility, interaction and citation information can be extracted from these. Based on these data, we investigate the patterns of scientific success as well as its connection with human mobility.

(FSA - Sciences de l'ingénieur)  – UCL, 2015

Advisors/Committee Members: UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, UCL - Ecole Polytechnique de Louvain, Blondel, Vincent, Lambiotte, Renaud, Jungers, Raphaël, Van Dooren, Paul, Smoreda, Zbigniew, Sinatra, Roberta.

Subjects/Keywords: Mobile phone data; Big data; Human mobility; Social interactions

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Deville, P. (2015). Understanding social dynamics through big data. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/165615

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Deville, Pierre. “Understanding social dynamics through big data.” 2015. Thesis, Université Catholique de Louvain. Accessed May 21, 2019. http://hdl.handle.net/2078.1/165615.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Deville, Pierre. “Understanding social dynamics through big data.” 2015. Web. 21 May 2019.

Vancouver:

Deville P. Understanding social dynamics through big data. [Internet] [Thesis]. Université Catholique de Louvain; 2015. [cited 2019 May 21]. Available from: http://hdl.handle.net/2078.1/165615.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Deville P. Understanding social dynamics through big data. [Thesis]. Université Catholique de Louvain; 2015. Available from: http://hdl.handle.net/2078.1/165615

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Université Catholique de Louvain

3. Browet, Arnaud. Algorithms for community and role detection in networks.

Degree: 2014, Université Catholique de Louvain

Many real networks encompass a community structure which means that nodes are organized in densely connected groups with, at the same time, relatively few links between the groups. We propose a new algorithm to uncover those community structures in very large networks based on an efficient representation of the most important interactions in the graph. This framework allows our algorithm to converge much faster than its competitors while extracting results of similar accuracy. Moreover, our method can efficiently use multiple processors synchronously to further decrease the computational time or to analyze larger networks. Yet, a partition in communities is not always representative of the actual distribution of the nodes. We consider a generalization of the problem of community detection, termed role extraction, which does not use any prior assumption on the links distribution in the graph. To extract a role structure, we describe a similarity measure based on the number of common neighbors between each pair of nodes and we propose an iterative scheme to compute a low-rank approximation of the similarity matrix. Our low-rank similarity measure has interesting properties that reveal characteristics of the role structure in benchmark and real graphs.

(FSA - Sciences de l)  – UCL, 2014

Advisors/Committee Members: UCL - SST/ICTM/INMA - Pôle en ingénierie mathématique, UCL - Ecole Polytechnique de Louvain, Van Dooren, Paul, Absil, Pierre-Antoine, Jungers, Raphael, Devleeschouwer, Christophe, Lambiotte, Renaud, Barahona, Mauricio.

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Browet, A. (2014). Algorithms for community and role detection in networks. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/151926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Browet, Arnaud. “Algorithms for community and role detection in networks.” 2014. Thesis, Université Catholique de Louvain. Accessed May 21, 2019. http://hdl.handle.net/2078.1/151926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Browet, Arnaud. “Algorithms for community and role detection in networks.” 2014. Web. 21 May 2019.

Vancouver:

Browet A. Algorithms for community and role detection in networks. [Internet] [Thesis]. Université Catholique de Louvain; 2014. [cited 2019 May 21]. Available from: http://hdl.handle.net/2078.1/151926.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Browet A. Algorithms for community and role detection in networks. [Thesis]. Université Catholique de Louvain; 2014. Available from: http://hdl.handle.net/2078.1/151926

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.