@article{fortunato2010community,
  abstract = {The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks. },
  author = {Fortunato, Santo},
  doi = {http://dx.doi.org/10.1016/j.physrep.2009.11.002},
  interhash = {9f6089e942903fc65309f77744c88109},
  intrahash = {fddddfb8990e8ea824c8c4b62244f737},
  issn = {0370-1573},
  journal = {Physics Reports },
  number = {3–5},
  pages = {75 - 174},
  title = {Community detection in graphs },
  url = {http://www.sciencedirect.com/science/article/pii/S0370157309002841},
  volume = 486,
  year = 2010
}

@article{fortunato2010,
  author = {Fortunato, Santo},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://arxiv.org/abs/0906.0612},
  interhash = {d87969055b829879e39240a2fb138a0f},
  intrahash = {9b4314b7c09d4a931c581e079e2be8ac},
  journal = {CoRR},
  title = {Community detection in graphs},
  volume = {abs/0906.0612},
  year = 2009
}

@inproceedings{Fortunato2009,
  acmid = {1698858},
  address = {ICST, Brussels, Belgium, Belgium},
  articleno = {27},
  author = {Fortunato, Santo and Lancichinetti, Andrea},
  booktitle = {Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools},
  interhash = {e7c7859a8b8e40cbe4d55bbb8b128e78},
  intrahash = {c23a9bb7b5d95b10aab7ec4124e71edf},
  isbn = {978-963-9799-70-7},
  location = {Pisa, Italy},
  numpages = {2},
  pages = {27:1--27:2},
  publisher = {ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering)},
  series = {VALUETOOLS '09},
  title = {Community Detection Algorithms: A Comparative Analysis: Invited Presentation, Extended Abstract},
  year = 2009
}

@article{Fortunato201075,
  abstract = {The modern science of networks has brought significant advances to our understanding of complex systems. One of the most relevant features of graphs representing real systems is community structure, or clustering, i.e. the organization of vertices in clusters, with many edges joining vertices of the same cluster and comparatively few edges joining vertices of different clusters. Such clusters, or communities, can be considered as fairly independent compartments of a graph, playing a similar role like, e.g., the tissues or the organs in the human body. Detecting communities is of great importance in sociology, biology and computer science, disciplines where systems are often represented as graphs. This problem is very hard and not yet satisfactorily solved, despite the huge effort of a large interdisciplinary community of scientists working on it over the past few years. We will attempt a thorough exposition of the topic, from the definition of the main elements of the problem, to the presentation of most methods developed, with a special focus on techniques designed by statistical physicists, from the discussion of crucial issues like the significance of clustering and how methods should be tested and compared against each other, to the description of applications to real networks.},
  author = {Fortunato, Santo},
  doi = {DOI: 10.1016/j.physrep.2009.11.002},
  interhash = {9f6089e942903fc65309f77744c88109},
  intrahash = {6901b5b9592c67a121ad6fd297aaa91e},
  issn = {0370-1573},
  journal = {Physics Reports},
  number = {3-5},
  pages = {75 - 174},
  title = {Community detection in graphs},
  url = {http://www.sciencedirect.com/science/article/B6TVP-4XPYXF1-1/2/99061fac6435db4343b2374d26e64ac1},
  volume = 486,
  year = 2010
}

@inproceedings{meiss2008ranking,
  abstract = {We analyze the traffic-weighted Web host graph obtained from a large sample of real Web users over about seven months. A number of interesting structural properties are revealed by this complex dynamic network, some in line with the well-studied boolean link host graph and others pointing to important differences. We find that while search is directly involved in a surprisingly small fraction of user clicks, it leads to a much larger fraction of all sites visited. The temporal traffic patterns display strong regularities, with a large portion of future requests being statistically predictable by past ones. Given the importance of topological measures such as PageRank in modeling user navigation, as well as their role in ranking sites for Web search, we use the traffic data to validate the PageRank random surfing model. The ranking obtained by the actual frequency with which a site is visited by users differs significantly from that approximated by the uniform surfing/teleportation behavior modeled by PageRank, especially for the most important sites. To interpret this finding, we consider each of the fundamental assumptions underlying PageRank and show how each is violated by actual user behavior},
  address = {New York, NY, USA},
  author = {Meiss, Mark R. and Menczer, Filippo and Fortunato, Santo and Flammini, Alessandro and Vespignani, Alessandro},
  booktitle = {WSDM '08: Proceedings of the international conference on Web search and web data mining},
  doi = {http://doi.acm.org/10.1145/1341531.1341543},
  interhash = {fa73cddce5412036c7aceb46da32b3de},
  intrahash = {7515e498f5a5cf00d23c0c8a92099118},
  isbn = {978-1-59593-927-9},
  location = {Palo Alto, California, USA},
  pages = {65--76},
  publisher = {ACM},
  title = {Ranking web sites with real user traffic},
  url = {http://portal.acm.org/citation.cfm?id=1341543},
  year = 2008
}