@misc{blondel2015survey,
  abstract = {In this paper, we review some advances made recently in the study of mobile
phone datasets. This area of research has emerged a decade ago, with the
increasing availability of large-scale anonymized datasets, and has grown into
a stand-alone topic. We will survey the contributions made so far on the social
networks that can be constructed with such data, the study of personal
mobility, geographical partitioning, urban planning, and help towards
development as well as security and privacy issues.},
  author = {Blondel, Vincent D. and Decuyper, Adeline and Krings, Gautier},
  interhash = {4386dfbc20b3f9e6a1a5bf113f5cdd1c},
  intrahash = {469e50f40c6091f639cff024f8e90100},
  note = {cite arxiv:1502.03406},
  title = {A survey of results on mobile phone datasets analysis},
  url = {http://arxiv.org/abs/1502.03406},
  year = 2015
}

@article{blondel2008fasta,
  abstract = {We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks.},
  author = {Blondel, Vincent D and Guillaume, Jean-Loup and Lambiotte, Renaud and Lefebvre, Etienne},
  groups = {public},
  interhash = {65254c1a703db2ce225cee4b56ea12ae},
  intrahash = {7855df1049bee476ad64ee3c29c29f0f},
  journal = {Journal of Statistical Mechanics: Theory and Experiment},
  localfile = {/home/aynaud/biblio/articles/louvain.pdf},
  number = 10,
  pages = {P10008 (12pp)},
  timestamp = {2009-09-21 01:52:25},
  title = {Fast unfolding of communities in large networks},
  username = {dbenz},
  volume = 2008,
  year = 2008
}

@article{blondel2004measure,
  abstract = {We introduce a concept of {similarity} between vertices of directed graphs. Let GA and GB be two directed graphs with, respectively, nA and nB vertices. We define an nB times nA similarity matrix S whose real entry sij expresses how similar vertex j (in GA) is to vertex i (in GB): we say that sij is their similarity score. The similarity matrix can be obtained as the limit of the normalized even iterates of Sk+1 = BSkAT + BTSkA, where A and B are adjacency matrices of the graphs and S0 is a matrix whose entries are all equal to 1. In the special case where GA = GB = G, the matrix S is square and the score sij is the similarity score between the vertices i and j of G. We point out that Kleinberg's "hub and authority" method to identify web-pages relevant to a given query can be viewed as a special case of our definition in the case where one of the graphs has two vertices and a unique directed edge between them. In analogy to Kleinberg, we show that our similarity scores are given by the components of a dominant eigenvector of a nonnegative matrix. Potential applications of our similarity concept are numerous. We illustrate an application for the automatic extraction of synonyms in a monolingual dictionary.},
  address = {Philadelphia, PA, USA},
  author = {Blondel, Vincent D. and Gajardo, Anah\'{\i} and Heymans, Maureen and Senellart, Pierre and Dooren, Paul Van},
  doi = {http://dx.doi.org/10.1137/S0036144502415960},
  interhash = {b59d33c99477e70a646615cd0470f459},
  intrahash = {fbaef7a3057ff12e16dfd65c42fb0239},
  issn = {0036-1445},
  journal = {SIAM Rev.},
  number = 4,
  pages = {647--666},
  publisher = {Society for Industrial and Applied Mathematics},
  title = {A Measure of Similarity between Graph Vertices: Applications to Synonym Extraction and Web Searching},
  url = {http://portal.acm.org/citation.cfm?id=1035533.1035557},
  volume = 46,
  year = 2004
}