%Original file available from http://www.cs.odu.edu/~jbollen/bibliographies/bibtex/socsci.bib
%Last update: Thursday 16 June 2005
%Current number of entries: 14

@article{analys:newman2004,
title = {Analysis of weighted network},
author = {M. E. J. Newman},
year = 2004,
journal = {Physical Review E},
volume = 70,
number= 5,
pages = {56131},
}


@article{measur:newman2005,
title = {A measure of betweenness centrality based on random walks},
author = {M. E. J. Newman},
year = 2005,
journal = {Social Networks},
volume = 27,
number = 1,
month = {January},
pages = {39--54},
}


@ARTICLE{emerg:barabasi1999,
title = {Emergence of scaling in random networks},
author = {Albert-Laszlo Barabasi and Reka Albert},
year = 1999,
journal = "Science", 
volume = 286,
pages = {509--512},
month = {October},
}


@ARTICLE{consen:stocker2001,
author = {Rob Stocker and David G. Green and David Newth},
year =  {2001},
title = {Consensus and cohesion in simulated social networks},
journal = {Journal of Artificial Societies and Social Simulation},
volume = 4,
number = 4,
url = {http://www.soc.surrey.ac.uk/JASSS/4/4/5.html},
}


@ARTICLE{uncloa:krebs2002,
author = {Valdis E. Krebs},
title = {Uncloaking Terrorist Networks},
year = 2001,
journal = {First Monday},
volume = 7,
number = 4,
month = {April},
url = {http://firstmonday.org/issues/issue7_4/krebs/index.html},
}

@inproceedings{maximi:kempe2003,
 author = {David Kempe and Jon Kleinberg and \&\#201;va Tardos},
 title = {Maximizing the spread of influence through a social network},
 booktitle = {Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining},
 year = {2003},
 isbn = {1-58113-737-0},
 pages = {137--146},
 location = {Washington, D.C.},
 doi = {http://netserv.lib.odu.edu:2378/10.1145/956750.956769},
 publisher = {ACM Press},
 }


@INPROCEEDINGS{linkpr:liben2003,
title = {The Link Prediction Problem for Social Networks},
author = {David Liben-Nowell and Jon Kleinberg},
year = 2003,
pages = {556--559},
booktitle = {{T}welfth {I}nternational {C}onference on {I}nformation and {K}nowledge {M}anagement},
month = {November},
abstract = {Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link prediction problem, and develop approaches to link prediction based on measures the "proximity" of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.},
location = {New Orleans, LA},
publisher = {{ACM}},
}


@ARTICLE{publis:dolado2003,
title = {Publishing performance in economics: Spanish rankings (1990-1999)},
author = {Juan J. Dolado and Antonio Garcia-Romero and Gema Zamarro},
year = 20003,
journal = {Span. Econ. Rev.},
volume = {5},
number = {4},
pages = {317},
abstract = {This paper contributes to the growing literature that analyses the Spanish publishing performance in Economics throughout the 1990s. Several bibliometric indicators are used in order to provide Spanish rankings (of both institutions and individual authors) based on Econlit journals. Further, lists of the ten most in uential authors and articles over that period, in terms of citations, are reported.},
}


@ARTICLE{scient1:newman2001,
title = {Scientific collaboration networks. {I}. {N}etwork construction and fundamental results},
author = {M. E. J. Newman},
year = {2001},
journal = {PHYSICAL REVIEW E},
volume = 64,
number = 1,
pages = {025102+},
abstract = {Using computer databases of scientific papers in physics, biomedical research, and computer science, we have constructed networks of collaboration between scientists in each of these disciplines. In these networks two scientists are considered connected if they have coauthored one or more papers together. We study a variety of statistical properties of our networks, including numbers of papers written by authors, numbers of authors per paper, numbers of collaborators that scientists have, existence and size of a giant component of connected scientists, and degree of clustering in the networks. We also highlight some apparent differences in collaboration patterns between the subjects studied. In the following paper, we study a number of measures of centrality and connectedness in the same networks.},
}

@ARTICLE{scient2:newman2001,
title = {Scientific collaboration networks. {II}.
Clustering and preferential attachment in growing networks},
author = {M. E. J. Newman},
year = {2001},
journal = {PHYSICAL REVIEW E},
volume = 64,
number = 2,
pages = {025102+},
abstract = {We study empirically the time evolution of scientific collaboration networks in physics
               and biology. In these networks, two scientists are considered connected if they have
               coauthored one or more papers together. We show that the probability of a pair of
               scientists collaborating increases with the number of other collaborators they have in
               common, and that the probability of a particular scientist acquiring new collaborators
               increases with the number of his or her past collaborators. These results provide
               experimental evidence in favor of previously conjectured mechanisms for clustering and
               power-law degree distributions in networks.},
}


@ARTICLE{power:bonacich1987,
title = {Power and centrality: A family of measures.},
author = {Phillip Bonacich},
journal = {American Journal of Sociology},
volume = 92,
number = 5,
year = 1987,
pages = {1170-1182},
}

@ARTICLE{eigenv:ruhnau2000,
title = {Eigenvector-centrality - a node-centrality?},
author = {B. Ruhnau},
year = 2000,
journal = {Social Networks},
volume = 22,
number = 4,
pages = {357--365},
abstract = {Networks of social relations can be represented by graphs and socio- or adjacency-matrices and their structure can be analyzed using different concepts, one of them called centrality. We will provide a new formalization of a "node-centrality" which leads to some properties a measure of centrality has to satisfy. These properties allow to test given measures, for example measures based on degree. closeness, betweenness or Bonacich's eigenvector-centrality. It turns out that it depends on normalization whether eigenvector-centrality does satisfy the expected properties or not.},
}

@ARTICLE{visual:nagpaul2002,
title = {Visualizing cooperation networks of elite institutions in India},
autor = {P. S. Nagpaul},
year = 2002,
journal = {Scientometrics},
volume = 54,
number = 2,
pages = {213--228},
abstract = {In this paper we have analyzed the pattern of cooperation links among fifty most prolific institutions (hereafter called "elite institutions") in India. The network of relationships among these institutions is sparse and more than two thirds of the cells in the collaboration matrix are empty. The network is centralized, but no institution dominates the network. It is only a set of few institutions that dominate the network. We have constructed a measure (Bonacich eigenvector centrality  index) to assess the position of each institution in the network. Barring a few notable exceptions, scientific size of an institution is directly related to its position in the network. We have graphically depicted the network of relationships among these institutions above a certain threshold of cooperation strength. The network incorporating 50 nodes and 171 arcs provides a synoptic view of bilateral relations among the institutions, but it is quite complex. We have therefore developed a block model of the network to assess the macro level features of cooperation links among the institutions. The block model indicates the isolation and marginality of certain clusters (or blocks) of institutions.},
}


@BOOK{ucinet:borgatti1999,
  author = {Steve P. Borgatti and M. G. Everett and L. C. Freeman},
  year = 1999,
  title = {{UCINET5}.0 Version 1.00},
  publisher = {Analytic Technologies}
}


@ARTICLE{quadra:hubert1976,
  author = {L. Hubert and J. Schultz},
  title = {Quadratic Assigment as a general data analysis strategy},
  journal = {British Journal of Mathematical and Statistical
                  Psychology},
  volume = 29,
  year = 1976,
  pages = {190--241},
}