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Manipulating Network Data

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Statistical Analysis of Network Data with R

Part of the book series: Use R! ((USE R,volume 65))

Abstract

We have seen that the term ‘network,’ broadly speaking, refers to a collection of elements and their inter-relations. The mathematical concept of a graph lends precision to this notion. We will introduce the basic elements of graphs—both undirected and directed—in Sect. 2.2 and discuss how to generate network graphs, both ‘by hand’ and from network data of various forms.

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Notes

  1. 1.

    Alternatively, there is within the network and sna packages, found in the statnet suite, a similarly rich set of tools for the manipulation and characterization of network graphs. These packages share nontrivial overlap with igraph.

  2. 2.

    Technically, a graph G is unique only up to relabellings of its vertices and edges that leave the structure unchanged. Two graphs that are equivalent in this sense are called isomorphic .

  3. 3.

    The exact representation of ‘igraph’ objects is not visible for the user and is subject to change.

  4. 4.

    This is the most basic visualization. We will explore the topic of visualization on its own in more depth in Chap. 3.

  5. 5.

    More generally, a weighted graph can be defined as a pair (V, E), where V is a set of vertices, as before, but the elements in E are now non-negative numbers, with one such number for each vertex pair. Analogously, the adjacency matrix A for a weighted graph is defined such that the entry A ij is equal to the corresponding weight for the vertex pair i and j.

  6. 6.

    In fact, the igraph data model is more general than described above, and allows for multi-graphs, with multiple edges between the same pair of vertices and edges from a vertex to itself.

  7. 7.

    The R code for generating this visualization is provided in Chap. 3.

References

  1. B. Bollobás, Modern Graph Theory (Springer, New York, 1998)

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  2. T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms (MIT Press, Cambridge, 2003)

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  3. R. Diestel, Graph Theory, 3rd edn. (Springer, Heidelberg, 2005)

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  4. J. Gross, J. Yellen, Graph Theory and Its Applications (Chapman & Hall/CRC, Boca Raton, 1999)

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  5. E. Lazega, The Collegial Phenomenon: The Social Mechanisms of Cooperation Among Peers in a Corporate Law Partnership (Oxford University Press, Oxford, 2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Mathematics and Statistics, Boston University Professor, Boston, MA, USA

    Eric D. Kolaczyk

  2. Department of Statistics, Harvard University Research Associate, Cambridge, MA, USA

    Gábor Csárdi

Authors
  1. Eric D. Kolaczyk
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  2. Gábor Csárdi
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© 2014 Springer Science+Business Media New York

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Kolaczyk, E.D., Csárdi, G. (2014). Manipulating Network Data. In: Statistical Analysis of Network Data with R. Use R!, vol 65. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0983-4_2

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