@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
}

@inproceedings{siersdorfer2009social,
  abstract = {The rapidly increasing popularity of Web 2.0 knowledge and content sharing systems and growing amount of shared data make discovering relevant content and finding contacts a difficult enterprize. Typically, folksonomies provide a rich set of structures and social relationships that can be mined for a variety of recommendation purposes. In this paper we propose a formal model to characterize users, items, and annotations in Web 2.0 environments. Our objective is to construct social recommender systems that predict the utility of items, users, or groups based on the multi-dimensional social environment of a given user. Based on this model we introduce recommendation mechanisms for content sharing frameworks. Our comprehensive evaluation shows the viability of our approach and emphasizes the key role of social meta knowledge for constructing effective recommendations in Web 2.0 applications.},
  address = {New York, NY, USA},
  author = {Siersdorfer, Stefan and Sizov, Sergej},
  booktitle = {HT '09: Proceedings of the 20th ACM conference on Hypertext and hypermedia},
  doi = {http://doi.acm.org/10.1145/1557914.1557959},
  interhash = {9245d0a556113aa107ba8171f3897156},
  intrahash = {d8c513959b1ac8f1da5b77bef87837da},
  isbn = {978-1-60558-486-7},
  location = {Torino, Italy},
  pages = {261--270},
  publisher = {ACM},
  title = {Social recommender systems for web 2.0 folksonomies},
  url = {http://portal.acm.org/citation.cfm?doid=1557914.1557959},
  year = 2009
}

@article{cilibrasi2007google,
  abstract = { Words and phrases acquire meaning from the way they are used in society, from their relative semantics to other words and phrases. For computers the equivalent of `society' is `database,' and the equivalent of `use' is `way to search the database.' We present a new theory of similarity between words and phrases based on information distance and Kolmogorov complexity. To fix thoughts we use the world-wide-web as database, and Google as search engine. The method is also applicable to other search engines and databases. This theory is then applied to construct a method to automatically extract similarity, the Google similarity distance, of words and phrases from the world-wide-web using Google page counts. The world-wide-web is the largest database on earth, and the context information entered by millions of independent users averages out to provide automatic semantics of useful quality. We give applications in hierarchical clustering, classification, and language translation. We give examples to distinguish between colors and numbers, cluster names of paintings by 17th century Dutch masters and names of books by English novelists, the ability to understand emergencies, and primes, and we demonstrate the ability to do a simple automatic English-Spanish translation. Finally, we use the WordNet database as an objective baseline against which to judge the performance of our method. We conduct a massive randomized trial in binary classification using support vector machines to learn categories based on our Google distance, resulting in an a mean agreement of 87% with the expert crafted WordNet categories.},
  author = {Cilibrasi, Rudi and Vitanyi, Paul M. B.},
  interhash = {8fc73a93c327ea9a45ef793242ac3508},
  intrahash = {00ba496f53767b92d5965db71eeea8bf},
  journal = {IEEE Transactions on Knowledge and Data Engineering},
  pages = 370,
  title = {The Google Similarity Distance},
  url = {http://www.citebase.org/abstract?id=oai:arXiv.org:cs/0412098},
  volume = 19,
  year = 2007
}

@inproceedings{hotho2006emergenta,
  abstract = {Social bookmark tools are rapidly emerging on the Web. In such systems users are setting up lightweight conceptual structures called folksonomies. The reason for their immediate success is the fact that no specific skills are needed for participating. In this paper we specify a formal model for folksonomies, briefly describe our own system BibSonomy, which allows for sharing both bookmarks and publication references, and discuss first steps towards emergent semantics.},
  address = {Bonn},
  author = {Hotho, Andreas and J�schke, Robert and Schmitz, Christoph and Stumme, Gerd},
  booktitle = {Informatik 2006 - Informatik f�r Menschen. Band 2},
  editor = {Hochberger, Christian and Liskowsky, R�diger},
  interhash = {7722035ca98379223f1f84eccbc91b3b},
  intrahash = {10f2225ed372712228cc543ee4d6af16},
  lastdatemodified = {2007-04-27},
  lastname = {Hotho},
  month = {October},
  note = {Proceedings of the Workshop on Applications of Semantic Technologies, Informatik 2006},
  own = {notown},
  read = {notread},
  series = {Lecture Notes in Informatics},
  title = {Emergent Semantics in BibSonomy},
  url = {http://www.kde.cs.uni-kassel.de/stumme/papers/2006/hotho2006emergent.pdf},
  volume = {P-94},
  year = 2006
}

@mastersthesis{benz2007collaborative,
  author = {Benz, Dominik},
  file = {benz2007collaborative.pdf:benz2007collaborative.pdf:PDF},
  interhash = {0b29ccf1782b6131160edbec1243e3c0},
  intrahash = {c6a6bc88abb5c9a43dd5a6071f5518de},
  lastdatemodified = {2007-04-28},
  lastname = {Benz},
  own = {notown},
  pdf = {benz07-collaborative.pdf},
  read = {notread},
  school = {Albert-Ludwigs-Universit{"a}t Freiburg, Department of Computer Science},
  title = {Collaborative Ontology Learning},
  year = 2007
}

@article{dupret2006principal,
  abstract = {We show that the singular value decomposition of a term similarity matrix induces a term hierarchy. This decomposition, usedin Latent Semantic Analysis and Principal Component Analysis for text, aims at identifying “conceptsâ€�? that can be used inplace of the terms appearing in the documents. Unlike terms, concepts are by construction uncorrelated and hence are lesssensitive to the particular vocabulary used in documents. In this work, we explore the relation between terms and conceptsand show that for each term there exists a latent subspace dimension for which the term coincides with a concept. By varyingthe number of dimensions, terms similar but more specific than the concept can be identified, leading to a term hierarchy.},
  author = {Dupret, Georges and Piwowarski, Benjamin},
  file = {:bast06-principal.pdf:PDF;dupret2006principal.pdf:dupret2006principal.pdf:PDF},
  groups = {public},
  interhash = {c1a309fb28731d35121b505f60e89ef1},
  intrahash = {b1f3cfc1d060423e224db9a0b0cdbbe6},
  journal = {String Processing and Information Retrieval},
  journalpub = {1},
  pages = {37--48},
  timestamp = {2007-10-22 13:37:16},
  title = {Principal Components for Automatic Term Hierarchy Building},
  url = {http://dx.doi.org/10.1007/11880561_4},
  username = {dbenz},
  year = 2006
}