@inproceedings{baur2007generating,
  abstract = {The modeling of realistic networks is of great importance for modern complex systems research. Previous procedures typically model the natural growth of networks by means of iteratively adding nodes, geometric positioning information, a definition of link connectivity based on the preference for nearest neighbors or already highly connected nodes, or combine several of these approaches. Our novel model is based on the well-know concept of k-cores, originally introduced in social network analysis. Recent studies exposed the significant k-core structure of several real world systems, e.g. the AS network of the Internet. We present a simple and efficient method for generating networks which strictly adhere to the characteristics of a given k-core structure, called core fingerprint. We show-case our algorithm in a comparative evaluation with two well-known AS network generators. },
  author = {Baur, Michael and Gaertler, Marco and Görke, Robert and Krug, Marcus and Wagner, Dorothea},
  booktitle = {Proceedings of the European Conference of Complex Systems},
  interhash = {387eebb80bbfaafab5ac201c88ebd263},
  intrahash = {e2fef8dce15087afbcc3489f2029d2c6},
  month = oct,
  title = {Generating Graphs with Predefined k-Core Structure},
  url = {http://i11www.ira.uka.de/extra/publications/bggkw-ggpcs-07.pdf},
  year = 2007
}

@article{chayes2013mathematics,
  abstract = {Dr Chayes’ talk described how, to a discrete mathematician, ‘all the world’s a graph, and all the people and domains merely vertices’. A graph is represented as a set of vertices V and a set of edges E, so that, for instance, in the World Wide Web, V is the set of pages and E the directed hyperlinks; in a social network, V is the people and E the set of relationships; and in the autonomous system Internet, V is the set of autonomous systems (such as AOL, Yahoo! and MSN) and E the set of connections. This means that mathematics can be used to study the Web (and other large graphs in the online world) in the following way: first, we can model online networks as large finite graphs; second, we can sample pieces of these graphs; third, we can understand and then control processes on these graphs; and fourth, we can develop algorithms for these graphs and apply them to improve the online experience.},
  author = {Chayes, Jennifer},
  doi = {10.1098/rsta.2012.0377},
  eprint = {http://rsta.royalsocietypublishing.org/content/371/1987/20120377.full.pdf+html},
  interhash = {3993b23ca636e9fb8497a1e918be7acf},
  intrahash = {3f77f26601231ba891aa65a702b8c867},
  journal = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
  number = 1987,
  title = {Mathematics of Web science: structure, dynamics and incentives},
  url = {http://rsta.royalsocietypublishing.org/content/371/1987/20120377.abstract},
  volume = 371,
  year = 2013
}

@book{dorling2012visualisation,
  abstract = {How do you draw a map of 100,000 places, of more than a million flows of people, of changes over time and space, of different kinds of spaces, surfaces and volumes, from human travel time to landscapes of hopes, fears, migration, manufacturing and mortality? How do you turn the millions of numbers concerning some of the most important moments of our lives into images that allow us to appreciate the aggregate while still remembering the detail? The visualization of spatial social structure means, literally, making visible the geographical patterns to the way our lives have come to be s.},
  address = {Hoboken},
  author = {Dorling, Danny},
  edition = {2nd},
  interhash = {10af4174b8276fd3a604a88e03b5656b},
  intrahash = {b024bc8aefe21e967777c3ebe62b5edb},
  isbn = {9781118354001 1118354001},
  publisher = {John Wiley & Sons},
  refid = {796383238},
  series = {Wiley Series in Computational and Quantitative Social Science},
  title = {The Visualisation of Spatial Social Structure},
  url = {http://public.eblib.com/EBLPublic/PublicView.do?ptiID=945112},
  year = 2012
}

@article{newman2006modularity,
  abstract = {Many networks of interest in the sciences, including social networks, computer networks, and metabolic and regulatory networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure is one of the outstanding issues in the study of networked systems. One highly effective approach is the optimization of the quality function known as “modularity” over the possible divisions of a network. Here I show that the modularity can be expressed in terms of the eigenvectors of a characteristic matrix for the network, which I call the modularity matrix, and that this expression leads to a spectral algorithm for community detection that returns results of demonstrably higher quality than competing methods in shorter running times. I illustrate the method with applications to several published network data sets.},
  author = {Newman, M. E. J.},
  doi = {10.1073/pnas.0601602103},
  interhash = {e664336d414a1e21d89f30cc56f5e739},
  intrahash = {5dd9d0c2155f242393e63547d8a2347f},
  journal = {Proceedings of the National Academy of Sciences},
  number = 23,
  pages = {8577--8582},
  title = {Modularity and community structure in networks},
  volume = 103,
  year = 2006
}

@article{white1981author,
  abstract = {It is shown that the mapping of a particular area of science, in this case information science, can be done using authors as units of analysis and the cocitations of pairs of authors as the variable that indicates their “distances” from each other. The analysis assumes that the more two authors are cited together, the closer the relationship between them. The raw data are cocitation counts drawn online from Social Scisearch (Social Sciences Citation Index) over the period 1972–1979. The resulting map shows (1) identifiable author groups (akin to “schools”) of information science, (2) locations of these groups with respect to each other, (3) the degree of centrality and peripherality of authors within groups, (4) proximities of authors within group and across group boundaries (“border authors” who seem to connect various areas of research), and (5) positions of authors with respect to the map's axes, which were arbitrarily set spanning the most divergent groups in order to aid interpretation. Cocitation analysis of authors offers a new technique that might contribute to the understanding of intellectual structure in the sciences and possibly in other areas to the extent that those areas rely on serial publications. The technique establishes authors, as well as documents, as an effective unit in analyzing subject specialties.},
  author = {White, Howard D. and Griffith, Belver C.},
  doi = {10.1002/asi.4630320302},
  interhash = {9d5d0acf1873abf4f57eddd875b8ad90},
  intrahash = {c44a512137b3e8f3f8c9c91e9c7b4a95},
  issn = {1097-4571},
  journal = {Journal of the American Society for Information Science},
  number = 3,
  pages = {163--171},
  publisher = {Wiley},
  title = {Author cocitation: A literature measure of intellectual structure},
  url = {http://dx.doi.org/10.1002/asi.4630320302},
  volume = 32,
  year = 1981
}