Definition of the Subject
A network is based on two sets: a set of vertices (nodes), that represent the selected units, and a set of lines (links), thatrepresent ties between units. Each line has two vertices as its end‐points; ifthey are equal it is called a loop . Vertices and lines forma graph . A line can be directed – an arc , or undirected – an edge .
Additional data about vertices or lines are usually known – their properties (attributes). Forexample: name/label, type, value, position, … In general
The data can be measured or computed.
Formally, a network \( {\mathcal{N}=(\mathcal{V},\mathcal{L},\mathcal{P},\mathcal{W}) } \) consists of the following:
-
A graph \( { \mathcal{G}=(\mathcal{V},\mathcal{L}) } \), where \( { \mathcal{V} } \) is the set of vertices and \( { \mathcal{L}=\mathcal{E}\cup\mathcal{A} } \). \( { \mathcal{E}\cap\mathcal{A} = \emptyset } \) is the set of lines. \( { \mathcal{A} } \) is the set of arcs and \( {...
Abbreviations
- Glossary:
-
For the basic notions on graphs and networks see the Articlea Wouter de Nooy: Social Network Analysis, Graph Theoretical Approaches to.
- Network:
-
consists of vertices linked by lines and additional data about vertices and/or lines.
- Network decomposition:
-
identification of parts of network and their interconnections. Usually it is described by a partition of set of vertices or set of lines.
- Time complexity of algorithm:
-
describes how the time needed to run the algorithm depends on the size of the input data.
- Reduction of network:
-
a network obtained by shrinking each cluster from a given partition into a vertex.
- Condensation:
-
a reduction for strong connectivity partition.
- Cut:
-
a subnetwork of vertices/lines with values of selected property above given threshold.
- Island:
-
a connected subnetwork of selected size of (locally) important, with respect to selected property, vertices/lines.
- Pattern searching:
-
identification of all appearances of selected small subnetwork (pattern or fragment) in a given network.
- Topological sort:
-
procedure to determine a compatible ordering in acyclic network.
Bibliography
Primary Literature
Abello J, Pardalos PM, Resende MG (2002) Handbook of Massive Data Sets. Springer, Heidelberg
Adamic LA, Lukose RM, Huberman BA (2002) Local Search in Unstructured Networks. In: Bornholdt S, Schuster HG (eds) Handbook of Graphs and Networks: From the Genome to the Internet. Wiley-VCH, Berlin
Ahmed A, Batagelj V, Fu X, Hong SH, Merrick D, Mrvar A (2007) Visualisation and Analysis of the Internet Movie Database. Proceedings of the Asia-Pacific Symposium on Visualisation (APVIS2007), Sydney, Australia, 5–7 Feb. IEEE, New York, pp 17–24
Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97
Alvarez-Hamelin JI, Dall'Asta L, Barrat A, Vespignani A (2005) k‑core decomposition: a tool for the visualization of large scale networks. cs.NI/0504107 published in Advances in Neural Information Processing Systems 18, Canada, 2006
Batagelj V (1989) Similarity measures between structured objects. In: Graovac A (ed) Proceedings of International Course and Conference on the Interfaces between Mathematics, Chemistry and Computer Science, Dubrovnik, 20–25 June 1988. Studies in Physical and Theoretical Chemistry, vol 63. Elsevier/North-Holland, Amsterdam, pp 25–40
Batagelj V, Brandes U (2005) Efficient Generation of Large Random Networks. Phys Rev E 71:036113
Batagelj V, Ferligoj A (2000) Clustering relational data. In: Gaul W, Opitz O, Schader M (eds) Data Analysis. Springer, Berlin, pp 3–15
Batagelj V, Mrvar A (2000) Some Analyses of Erdős Collaboration Graph. Soc Netw 22:173–186
Batagelj V, Mrvar A (2001) A Subquadratic Triad Census Algorithm for Large Sparse Networks with Small Maximum Degree. Soc Netw 23:237–43
Batagelj V, Mrvar A (2007) Hierarchical clustering with relational constraints of large data sets. 6th Slovenian International Conference on Graph Theory, Bled, 24–30 June
Batagelj V, Mrvar A (2008) Analysis of kinship relations with Pajek. Soc Sci Comput Rev 26(2):224–246
Batagelj V, Zaveršnik M (2002) Generalized Cores. arxiv cs.DS/0202039
Batagelj V, Zaveršnik M (2007) Short cycle connectivity. Discret Math 307(3–5):310–318
Bollobás B (2001) Random Graphs. Cambridge University Press, Cambridge
Brandes U (2001) A Faster Algorithm for Betweenness Centrality. J Math Soc 25(2):163–177
Breiger RL (2004) The analysis of social networks. In: Hardy M, Bryman A (eds) Handbook of data analysis. Sage, London, pp 505–526
Chakrabarti D, Zhan Y, Faloutsos C (2004) R-MAT: A Recursive Model for Graph Mining. In: SIAM Data Mining 2004, Orlando, Florida, SIAM
Cormen TH, Leiserson CE, Rivest RL, Stein C (2001) Introduction to algorithms. MIT Press, Cambridge
Doreian P, Batagelj V, Ferligoj A (2000) Symmetric-Acyclic Decompositions of Networks. J Classif 17(1):3–28
Doreian P, Batagelj V, Ferligoj A (2005) Generalized Blockmodeling. Cambridge University Press, Cambridge
Dorogovtsev SN, Mendes JFF (2003) Evolution of networks: from biological nets to the internet and www. Oxford University Press, Oxford
Ferligoj A, Batagelj V (1983) Some types of clustering with relational constraints. Psychometrika 48(4):541–552
Freeman LC (1979) Centrality in Social Networks: A Conceptual Clarification. Soc Netw 1:211–213
Garfield E, Sher IH, Torpie RJ (1964) The Use of Citation Data in Writing the History of Science. The Institute for Scientific Information, Philadelphia
Granovetter M (1973) The Strength of Weak Ties. Am J Sociol 78:1360–80
Harary F, Norman RZ, Cartwright D (1965) Structural Models: An Introduction to the Theory of Directed Graphs. Wiley, New York
Huisman M, van Duijn MAJ (2005) Software for social network analysis. In: Carrington PJ, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, Cambridge, pp 270–316
Hummon NP, Doreian P (1990) Computational Methods for Social Network Analysis. Soc Netw 12:273–288
Kleinberg J (1998) Authoritative sources in a hyperlinked environment. Proc 9th ACM-SIAM Symposium on Discrete Algorithms
Kleinberg J, Kumar R, Raghavan P, Rajagopalan S, Tomkins A (1999) The Web as a graph: measurements, models and methods. Proc of the 5th International Computing and combinatorics Conference
Leskovec J, Kleinberg J, Faloutsos C (2006) Laws of Graph Evolution: Densification and Shrinking Diameters. ACM Transactions on Knowledge Discovery from Data (TKDD) vol 1, issue 1, article 2
Li L, Alderson D, Tanaka R, Doyle JC, Willinger W (2007) Towards a Theory of Scale-Free Graphs: Definition, Properties, and Implications. cond-mat/0501169, Internet Math 2(4):431–523
Mane KK, Börner K (2004) Mapping topics and topic bursts in PNAS. Proc Natl Acad Sci USA 101:5287–5290
Newman MEJ (2003) The structure and function of complex networks. SIAM Rev 45:167–256
Newman MEJ, Barabási AL, Watts D (2006) The Structure and Dynamics of Networks. Princeton Studies in Complexity. Princeton University Press, Princeton
Pennock DM, Flake GW, Lawrence S, Glover EJ, Giles CL (2002) Winners don't take all: Characterizing the competition for links on the web. Proc Natl Acad Sci USA 99(8):5207–5211
Seidman SB (1983) Network Structure And Minimum Degree. Soc Netw 5:269–287
Schank T, Wagner D (2005) Finding, counting and listing all triangles in large graphs, an experimental study. In: Workshop on Experimental and Efficient Algorithms (WEA). Lecture Notes in Computer Science, vol 3503, Springer, pp 606–609
Snyder D, Kick E (1979) The World System and World Trade: An Empirical Exploration of Conceptual Conflicts. Sociol Q 20(1):23–36
Snijders TAB (2005) Models for Longitudinal Network Data. In: Carrington P, Scott J, Wasserman S (eds) Models and methods in social network analysis. Cambridge University Press, New York
Stuckenschmidt H, Klein M (2004) Structure-Based Partitioning of Large Concept Hierarchies. Proc of the 3rd International Semantic Web Conference ISWC 2004, Hiroshima, Japan
Wasserman S, Faust K (1994) Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge
White DR, Batagelj V, Mrvar A (1999) Analyzing Large Kinship and Marriage Networks with Pgraph and Pajek. Soc Sci Comput Rev 17:245–274
Zaveršnik M, Batagelj V (2004) Islands. Slides from Sunbelt XXIV, Portorož, Slovenia, 12–16 May
Books and Reviews
Ahuja RK, Magnanti TL, Orlin JB (1993) Network Flows: Theory, Algorithms, and Applications. Prentice Hall, Englewood Cliffs
Batagelj V, Mrvar A (2003) Pajek – Analysis and Visualization of Large Networks. In: Jünger M, Mutzel P (eds) Graph Drawing Software. Springer, Berlin, pp 77–103
Brandes U, Erlebach T (2005) Network Analysis: Methodological Foundations. Lecture Notes in Computer Science. Springer, Berlin
Carrington PJ, Scott J, Wasserman S (2005) Models and Methods in Social Network Analysis. Cambridge University Press, Cambridge
Degenne A, Forsé M (1999) Introducing Social Networks. SAGE Publications, London
de Nooy W, Mrvar A, Batagelj V (2005) Exploratory Social Network Analysis with Pajek. Cambridge University Press, Cambridge
Knuth DE (1993) The Stanford GraphBase: A Platform for Combinatorial Computing. Addison-Wesley, Reading
Scott JP (2000) Social Network Analysis: A Handbook. SAGE Publications, London
Web Resources
Center for Complex Network Research, Notre Dame: http://www.nd.edu/ networks/
Center for Spatially Integrated Social Science: http://www.csiss.org/
Complex Networks Collaboratory: http://cxnets.googlepages.com/
Internet Movie Database http://www.imdb.com/
Matthieu Latapy. Triangle computation web page. http://www-rp.lip6.fr/ latapy/Triangles/
Netminer: http://www.netminer.com/
Pajek: http://pajek.imfm.si data sets: http://vlado.fmf.uni-lj.si/pub/networks/data/
The Edinburgh Associative Thesaurus: http://www.eat.rl.ac.uk/
The Kansas Event Data System: http://web.ku.edu/keds/
UCINET: http://www.analytictech.com/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag
About this entry
Cite this entry
Batagelj, V. (2009). Social Network Analysis, Large-Scale. In: Meyers, R. (eds) Encyclopedia of Complexity and Systems Science. Springer, New York, NY. https://doi.org/10.1007/978-0-387-30440-3_489
Download citation
DOI: https://doi.org/10.1007/978-0-387-30440-3_489
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-75888-6
Online ISBN: 978-0-387-30440-3
eBook Packages: Physics and AstronomyReference Module Physical and Materials ScienceReference Module Chemistry, Materials and Physics