
editor={Leonardi, Stefano},
@incollection{trank2004,
year={2004},
isbn={978-3-540-23427-2},
booktitle={Algorithms and Models for the Web-Graph},
volume={3243},
series={Lecture Notes in Computer Science},

doi={10.1007/978-3-540-30216-2_11},
title={{T-Rank: Time-Aware Authority Ranking}},
url={http://dx.doi.org/10.1007/978-3-540-30216-2_11},
publisher={Springer},
author={Berberich, Klaus and Vazirgiannis, Michalis and Weikum, Gerhard},
pages={131-142}
}

@article{laender2002,
 author    = {Alberto H. F. Laender and
              Berthier A. Ribeiro-Neto and
              Altigran Soares da Silva and
              Juliana S. Teixeira},
 title     = {{A Brief Survey of Web Data Extraction Tools}},
 journal   = {SIGMOD Record},
 volume    = {31},
 number    = {2},
 year      = {2002},
 pages     = {84-93}
},

@article{braha_centrality_2006,
	title = {From centrality to temporary fame: Dynamic centrality in complex networks},
	volume = {12},
	number = {2},
	journal = {Complexity},
	author = {Braha, D. and {Bar-Yam}, Y.},
	year = {2006},
	pages = {59-63}
},

	series = {{KDD} '03},
@inproceedings{white_algorithms_2003,
	title = {{Algorithms for Estimating Relative Importance in Networks}},
	isbn = {1-58113-737-0},
	location = {Washington, {D.C.}, USA},
	url = {http://doi.acm.org/10.1145/956750.956782},
	doi = {http://doi.acm.org/10.1145/956750.956782},
	booktitle = {Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
	author = {White, Scott and Smyth, Padhraic},
	year = {2003},
	keywords = {graphs, Markov chains, {PageRank}, relative importance, social networks},
	pages = {266-275}
},

%journal = {{ArXiv} e-prints},
volume = {abs/1108.0261},
month = aug,
@inproceedings{cotta_fifa_2011,
	title = {{FIFA World Cup 2010: A Network Analysis of the Champion Team Play}},
	booktitle = {Proceedings of the 1st International Workshop on Complex Systems in Sports},
	author = {Cotta, C. and Mora, A. M and {Merelo-Molina}, C. and Julin Merelo, J.},
	year = {2011},
	address = {Paris, {France}},
	keywords = {Computer Science - Social and Information Networks, Physics - Physics and Society}
},

@article{onody_complex_2004,
	title = {{Complex network study of Brazilian soccer players}},
	volume = {70},
	doi = {10.1103/PhysRevE.70.037103},
	number = {3},
	journal = {Phys. Rev. E},
	author = {Onody, Roberto N and de Castro, Paulo A},
	month = sep,
	year = {2004},
	pages = {037103}
},

@article{tang_exploiting_2010,
	title = {Exploiting Temporal Complex Network Metrics in Mobile Malware Containment},
	journal = {{ArXiv} e-prints},
	author = {Tang, J. and Mascolo, C. and Musolesi, M. and Latora, V.},
	month = dec,
	year = {2010},
	keywords = {Computer Science - Networking and Internet Architecture, Physics - Physics and Society}
},

@inproceedings{dai_freshness_2010,
	series = {{SIGIR} '10},
	title = {Freshness matters: in flowers, food, and web authority},
	isbn = {978-1-4503-0153-4},
	location = {Geneva, Switzerland},
	url = {http://doi.acm.org/10.1145/1835449.1835471},
	doi = {http://doi.acm.org/10.1145/1835449.1835471},
	booktitle = {Proceeding of the 33rd international {ACM} {SIGIR} conference on Research and development in information retrieval},
	publisher = {{ACM}},
	author = {Dai, Na and Davison, Brian D},
	year = {2010},
	keywords = {{PageRank}, temporal link analysis, web freshness, web search engine},
	pages = {114-121}
},

@inproceedings{brandes_networks_2009,
	address = {Washington, {DC}, {USA}},
	title = {Networks Evolving Step by Step: Statistical Analysis of Dyadic Event Data},
	isbn = {978-0-7695-3689-7},
	url = {http://portal.acm.org/citation.cfm?id=1602240.1602687},
	doi = {10.1109/ASONAM.2009.28},
	booktitle = {Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining},
	publisher = {{IEEE} Computer Society},
	author = {Brandes, Ulrik and Lerner, Jrgen and Snijders, Tom A. B},
	year = {2009},
	keywords = {event data, longitudinal analysis, political networks, signed networks, social networks, structural balance theory},
	pages = {200-205}
},

@inproceedings{omadadhain_eventrank:_2005,
	address = {New York, {NY}, {USA}},
	series = {{LinkKDD} '05},
	title = {{EventRank:} a framework for ranking time-varying networks},
	isbn = {1-59593-215-1},
	location = {Chicago, Illinois},
	url = {http://doi.acm.org/10.1145/1134271.1134273},
	doi = {http://doi.acm.org/10.1145/1134271.1134273},
	booktitle = {Proceedings of the 3rd international workshop on Link discovery},
	publisher = {{ACM}},
	author = {{O'Madadhain}, Joshua and Smyth, Padhraic},
	year = {2005},
	keywords = {network ranking algorithms, network temporal evolution, Social network analysis},
	pages = {9?16}
},

@inproceedings{tang_social_2009,
	address = {New York, {NY}, {USA}},
	series = {{KDD} '09},
	title = {Social influence analysis in large-scale networks},
	isbn = {978-1-60558-495-9},
	location = {Paris, France},
	url = {http://doi.acm.org/10.1145/1557019.1557108},
	doi = {http://doi.acm.org/10.1145/1557019.1557108},
	booktitle = {Proc. of {ACM SIGKDD}},
	publisher = {{ACM}},
	author = {Tang, Jie and Sun, Jimeng and Wang, Chi and Yang, Zi},
	year = {2009},
	keywords = {large-scale network, social influence analysis, social networks, topical analysis propagation},
	pages = {807?816}
},

@article{chen_finding_2007,
	title = {Finding scientific gems with Google's {PageRank} algorithm},
	volume = {1},
	issn = {1751-1577},
	url = {http://www.sciencedirect.com/science/article/pii/S1751157706000034},
	doi = {DOI: 10.1016/j.joi.2006.06.001},
	abstract = {We apply the Google {PageRank} algorithm to assess the relative importance of all publications in the Physical Review family of journals from 1893 to 2003. While the Google number and the number of citations for each publication are positively correlated, outliers from this linear relation identify some exceptional papers or},
	number = {1},
	journal = {Journal of Informetrics},
	author = {Chen, P. and Xie, H. and Maslov, S. and Redner, S.},
	year = {2007},
	keywords = {Citations},
	pages = {8 -- 15}
}

@article{braha_centrality_2006-1,
	title = {From centrality to temporary fame: Dynamic centrality in complex networks: Research Articles},
	volume = {12},
	issn = {1076-2787},
	url = {http://portal.acm.org/citation.cfm?id=1182969.1182975},
	doi = {10.1002/cplx.v12:2},
	number = {2},
	journal = {Complex.},
	author = {Braha, Dan and {Bar-Yam}, Yaneer},
	month = nov,
	year = {2006},
	keywords = {dynamic hubs, multiscale networks, social networks, time-based networks, turbulent networks},
	pages = {59?63}
}

 series = {KDD '08},
@inproceedings{vaz_de_melo_can_2008,
	title = {{Can Complex Network Metrics Predict the Behavior of {NBA} Teams?}},
	author = {Vaz de Melo, Pedro O. S. and Almeida, Virgilio A. F. and Loureiro, Antonio A. F.},
	year = {2008},
	pages = {695--703},
	location = {Las Vegas, Nevada, USA},
	booktitle = {Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},

}

@misc{_football_????,
	title = {Football Rankings Versus Google's {PageRank} - {IEEE} Spectrum},
	url = {http://spectrum.ieee.org/podcast/at-work/innovation/football-rankings-versus-googles-pagerank},
	howpublished = {http://spectrum.ieee.org/podcast/at-work/innovation/football-rankings-versus-googles-pagerank}
}

@article{kudelka2011,
	title = {Social and swarm aspects of co-authorship network},
	url = {http://jigpal.oxfordjournals.org/content/early/2011/02/08/jigpal.jzr002.abstract},
	doi = {10.1093/jigpal/jzr002},
	abstract = {The analysis of social networks is concentrated mainly on uncovering hidden relations and properties of network nodes (vertices). Most of the current approaches are focused on different network types and different network coefficients. This article introduces a social network analysis based on the so-called Forgetting Curve and Swarm Intelligence inspired by the Ant Colony Optimization. We analyse a co-authorship network and identify two types of ties among its nodes. The Forgetting Curve and Swarm Intelligence are used to model the dynamics of such a network.},
	journal = {Logic Journal of {IGPL}},
	author = {Kudelka, Milos and Hork, Zdenek and Snasel, Vaclav and Kromer, Pavel and Platos, Jan and Abraham, Ajith},
	volume = {20}, 
	number = {3}, 
	pages = {634-643}, 
	year = {2012}
}

@article{govan_generalizing_????,
	title = {Generalizing google's pagerank to rank national football league teams},
	volume = {2008},
	url = {http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Generalizing+Google?s+PageRank+to+Rank+National+Football+League+Teams#0},
	journal = {Proceedings of the {SAS} Global Forum},
	author = {Govan, Anjela Y and Meyer, Carl D and Albright, Russell},
	pages = {1?11}
}

@article{lue_leaders_2011,
	title = {Leaders in Social Networks, the {{\textless}italic{\textgreater}Delicious{\textless}/italic{\textgreater}} Case},
	volume = {6},
	url = {http://dx.doi.org/10.1371%2Fjournal.pone.0021202},
	doi = {10.1371/journal.pone.0021202},
	abstract = {{{\textless}p{\textgreater}Finding} pertinent information is not limited to search engines. Online communities can amplify the influence of a small number of power users for the benefit of all other users. Users' information foraging in depth and breadth can be greatly enhanced by choosing suitable leaders. For instance in {\textless}ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="http://delicious.com" xlink:type="simple"{\textgreater}delicious.com{\textless}/ext-link{\textgreater}, users subscribe to leaders' collection which lead to a deeper and wider reach not achievable with search engines. To consolidate such collective search, it is essential to utilize the leadership topology and identify influential users. Google's {PageRank}, as a successful search algorithm in the World Wide Web, turns out to be less effective in networks of people. We thus devise an adaptive and parameter-free algorithm, the {LeaderRank}, to quantify user influence. We show that {LeaderRank} outperforms {PageRank} in terms of ranking effectiveness, as well as robustness against manipulations and noisy data. These results suggest that leaders who are aware of their clout may reinforce the development of social networks, and thus the power of collective search.{\textless}/p{\textgreater}},
	number = {6},
	journal = {{PLoS} {ONE}},
	author = {L, Linyuan and Zhang, {Yi-Cheng} and Yeung, Chi Ho and Zhou, Tao},
	year = {2011},
	pages = {e21202}
},

@article{petersen_quantitative_2010,
	title = {Quantitative and empirical demonstration of the Matthew effect in a study of career longevity},
	url = {http://www.pnas.org/content/early/2010/12/16/1016733108.abstract},
	doi = {10.1073/pnas.1016733108},
	abstract = {The Matthew effect refers to the adage written some two-thousand years ago in the Gospel of St. Matthew: {?For} to all those who have, more will be given.? Even two millennia later, this idiom is used by sociologists to qualitatively describe the dynamics of individual progress and the interplay between status and reward. Quantitative studies of professional careers are traditionally limited by the difficulty in measuring progress and the lack of data on individual careers. However, in some professions, there are well-defined metrics that quantify career longevity, success, and prowess, which together contribute to the overall success rating for an individual employee. Here we demonstrate testable evidence of the age-old Matthew ?rich get richer? effect, wherein the longevity and past success of an individual lead to a cumulative advantage in further developing his or her career. We develop an exactly solvable stochastic career progress model that quantitatively incorporates the Matthew effect and validate our model predictions for several competitive professions. We test our model on the careers of 400,000 scientists using data from six high-impact journals and further confirm our findings by testing the model on the careers of more than 20,000 athletes in four sports leagues. Our model highlights the importance of early career development, showing that many careers are stunted by the relative disadvantage associated with inexperience.},
	journal = {Proc. of the National Academy of Sciences},
	author = {Petersen, Alexander M. and Jung, {Woo-Sung} and Yang, {Jae-Suk} and Stanley, H. Eugene},
	year = {2010}
},

@misc{_mixed_wiki,
	title = {Mixed martial arts - Wikipedia, the free encyclopedia},
	url = {http://en.wikipedia.org/wiki/Mixed_martial_arts},
	howpublished = {{http://en.wikipedia.org/wiki/Mixed\_martial\_arts}}
},

@misc{_ufc_noticia,
	title = {{UFC} Rio: nmeros to grandes quanto os lutadores - Esportes - {EXAME.com}},
	url = {http://exame.abril.com.br/estilo-de-vida/esportes/noticias/ufc-rio-numeros-tao-grandes-quanto-os-lutadores},
	howpublished = {http://exame.abril.com.br/estilo-de-vida/esportes/noticias/ufc-rio-numeros-tao-grandes-quanto-os-lutadores}
},


 month = {02}
@article{radicchi_who_2011,
	title = {{Who is the best player ever? A complex network analysis of the history of professional tennis}},
	volume = {6},
	shorttitle = {Who is the best player ever?},
	number = {2},
    journal = {PLoS ONE},
    publisher = {Public Library of Science},
	author = {Radicchi, Filippo},
	year = {2011},
	pages = {e17249},
    volume = {6},
   
},

@article{heidemann_identifying_2010,
	title = {Identifying Key Users in Online Social Networks: A {PageRank} Based Approach},
	shorttitle = {Identifying Key Users in Online Social Networks},
	author = {Heidemann, J. and Klier, M. and Probst, F.},
	year = {2010}
},

@inproceedings{govan_generalizing_2008,
	title = {Generalizing google?s pagerank to rank national football league teams},
	volume = {2008},
	booktitle = {Proc. of the {SAS} Global Forum},
	author = {Govan, A. and Meyer, C. D and Albright, R.},
	year = {2008}
},

@article{dinkar_time_2011,
	title = {Time Aware Incremental Page Ranking based on Personalized Link Structure Analysis},
	volume = {2},
	number = {2},
	journal = {International Journal of Research and Reviews in Computer Science},
	author = {Dinkar, S. K},
	year = {2011}
},

@article{ding_applying_2011,
	title = {Applying weighted {PageRank} to author citation networks},
	journal = {Journal of the American Society for Information Science and Technology},
	author = {Ding, Y.},
	year = {2011}
},

@article{omadadhain_prediction_2005,
	title = {Prediction and ranking algorithms for event-based network data},
	volume = {7},
	number = {2},
	journal = {{ACM} {SIGKDD} Explorations Newsletter},
	author = {{O'Madadhain}, J. and Hutchins, J. and Smyth, P.},
	year = {2005},
	pages = {23-30}
},

@inproceedings{tylenda_towards_2009,
	title = {Towards time-aware link prediction in evolving social networks},
	booktitle = {Proc. of the 3rd Workshop on Social Network Mining and Analysis},
	author = {Tylenda, T. and Angelova, R. and Bedathur, S.},
	year = {2009},
	pages = {9}
}



@techreport{page_pagerank_1999,
	type = {Technical Report},
	title = {{The {PageRank} Citation Ranking: Bringing Order to the Web.}},
	number = {1999-66},
	institution = {Stanford {InfoLab}},
	author = {Page, Lawrence and Brin, Sergey and Motwani, Rajeev and Winograd, Terry},
	month = nov,
	year = {1999},
}

@article{boccaletti_complex_2006,
	title = {Complex networks: Structure and dynamics},
	volume = {424},
	issn = {0370-1573},
	url = {http://www.sciencedirect.com/science/article/pii/S037015730500462X},
	doi = {10.1016/j.physrep.2005.10.009},
	abstract = {Coupled biological and chemical systems, neural networks, social interacting species, the Internet and the World Wide Web, are only a few examples of systems composed by a large number of highly interconnected dynamical units. The first approach to capture the global properties of such systems is to model them as graphs whose nodes represent the dynamical units, and whose links stand for the interactions between them. On the one hand, scientists have to cope with structural issues, such as characterizing the topology of a complex wiring architecture, revealing the unifying principles that are at the basis of real networks, and developing models to mimic the growth of a network and reproduce its structural properties. On the other hand, many relevant questions arise when studying complex networks? dynamics, such as learning how a large ensemble of dynamical systems that interact through a complex wiring topology can behave collectively. We review the major concepts and results recently achieved in the study of the structure and dynamics of complex networks, and summarize the relevant applications of these ideas in many different disciplines, ranging from nonlinear science to biology, from statistical mechanics to medicine and engineering.},
	number = {4?5},
	journal = {Physics Reports},
	author = {Boccaletti, S. and Latora, V. and Moreno, Y. and Chavez, M. and Hwang, {D.-U.}},
	year = {2006},
	pages = {175 -- 308}
}


publisher={Springer Berlin Heidelberg},
isbn={978-3-540-22354-2},
editor={Ben-Naim, Eli and Frauenfelder, Hans and Toroczkai, Zoltan},
doi={10.1007/978-3-540-44485-5_16},
title={Who Is the Best Connected Scientist?A Study of Scientific Coauthorship Networks},
url={http://dx.doi.org/10.1007/978-3-540-44485-5_16},
@incollection{newman2004,
    author = "M. E. J. Newman",
	booktitle={Complex Networks},
    pages = {337--370},
    title = {{Who Is the Best Connected Scientist? A Study of Scientific Coauthorship Networks}},
    year = {2004},
    publisher = "Springer",
    year={2004},
    volume={650},
	series={Lecture Notes in Physics}
}

@article{pires2011,
	title = {Ranking in collaboration networks using a group based metric},
	journal = {J. Braz. Comp. Soc.},
	volume = {17},
	number = {4},
        pages = {255--266},
	author = {Vincius P. Freire and Daniel R. Figueiredo},
	year = {2011}
}

@book{Newman2010,
  author =       {M. E. J. Newman},
  title =        {Networks: An Introduction},
  publisher =    {Oxford University Press},
  year =         {2010}
}

publisher = {ACM},
address = {New York, NY, USA},
numpages = {10},
series = {WSDM '08},
@InProceedings{huang2008,
        author = "Jian Huang and Ziming Zhuang and Jia Li and C. Lee Giles",
        title = {{Collaboration over Time: Characterizing and Modeling Network Evolution}},
        author = {Huang, Jian and Zhuang, Ziming and Li, Jia and Giles, C. Lee},
 		booktitle = {Proceedings of the International Conference on Web Search and Web Data Mining},
		
		year = {2008},
		isbn = {978-1-59593-927-2},
		location = {Palo Alto, California, USA},
		pages = {107--116},

		url = {http://doi.acm.org/10.1145/1341531.1341548},
		doi = {10.1145/1341531.1341548},
		acmid = {1341548},
		
		keywords = {network evolution, social network analysis (SNA), stochastic network modeling},
}

@article{barabasi2009,
	title = {Scale-Free Networks: A Decade and Beyond},
	author = {Albert-László Barab{\'a}si},
	journal = {Science},
	volume = {325},
	number = {5939},
        pages = {412--413},
	year = {2009}
}

@InProceedings{menezes2009,
        title = "A geographical analysis of knowledge production in computer science",
        booktitle = "{WWW'09}",
        author = "G. V. Menezes and N. Ziviani and A. H. F. Laender and V. A. F. Almeida",
        pages = "1041--1050",
}

@article{barabasi2002,
title = "Evolution of the social network of scientific collaborations",
journal = "Physica A",
volume = "311",
number = "3-4",
pages = "590--614",
year = "2002",
author = "Albert-László Barab{\'a}si and H. Jeong and Z. Nda and E. Ravasz and A. Schubert and T. Vicsek"
}

@article {nowell2007,
author = {Liben-Nowell, David and Kleinberg, Jon},
title = {The link-prediction problem for social networks},
journal = {JASIST},
volume = {58},
number = {7},
pages = {1019--1031},
year = {2007},
}

numpages = {12},
publisher = {Springer},
address = {Berlin, Heidelberg},
 editor    = {Konstantin Avrachenkov and
               Debora Donato and
               Nelly Litvak}
 series = {WAW '09},
@inproceedings{mourao09,
 author = {Mour\~{a}o, Fernando and Rocha, Leonardo and Miranda, Lucas and Almeida, Virg\'{\i}lio and Meira,Jr., Wagner},
 title = {Quantifying the Impact of Information Aggregation on Complex Networks: A Temporal Perspective},
 booktitle = {Proceedings of the 6th International Workshop on Algorithms and Models for the Web-Graph},

 year = {2009},
 isbn = {978-3-540-95994-6},
 location = {Barcelona, Spain},
 pages = {50--61},
 url = {http://dx.doi.org/10.1007/978-3-540-95995-3_5},
 doi = {10.1007/978-3-540-95995-3_5},
 acmid = {1506850}
}

 series = {WebKDD/SNA-KDD '07},
@inproceedings{sharan07,
 author = {Sharan, Umang and Neville, Jennifer},
 title = {Exploiting time-varying relationships in statistical relational models},
 booktitle = {Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis},

 year = {2007},
 isbn = {978-1-59593-848-0},
 location = {San Jose, California, USA},
 pages = {9--15},
 url = {http://doi.acm.org/10.1145/1348549.1348551},
 doi = {10.1145/1348549.1348551},
 acmid = {1348551},
}

@article{watts06,
    author = {Kossinets, Gueorgi and Watts, Duncan J.},
    doi = {10.1126/science.1116869},
    journal = {Science},
    number = {5757},
    pages = {88--90},
    title = {{Empirical Analysis of an Evolving Social Network}},
    volume = {311},
    year = {2006}
}

publisher = {ACM},
 address = {New York, NY, USA},
  numpages = {9},
   series = {KDD '08},
@inproceedings{leskovec08,
 author = {Leskovec, Jure and Backstrom, Lars and Kumar, Ravi and Tomkins, Andrew},
 title = {Microscopic evolution of social networks},
 booktitle = {Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},

 year = {2008},
 isbn = {978-1-60558-193-4},
 location = {Las Vegas, Nevada, USA},
 pages = {462--470},
 url = {http://doi.acm.org/10.1145/1401890.1401948},
 doi = {10.1145/1401890.1401948},
 acmid = {1401948},
 keywords = {graph generators, maximum likelihood, network evolution, social networks, transitivity, triadic closure},
} 

@article{DBLP:journals/sigkdd/GetoorD05a,
  author    = {Lise Getoor and
               Christopher P. Diehl},
  title     = {{Link Mining: A Survey}},
  journal   = {SIGKDD Explorations},
  volume    = {7},
  number    = {2},
  year      = {2005},
  pages     = {3-12}
}

}

