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Random Graphs as Null Models

  • Katharina A. ZweigEmail author
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

In the last chapter, a qualitative comparison of various real-world structures with classic random graph models revealed that complex networks are non-random in many aspects. This chapter focuses on the question of how to quantify the statistical significance of an observed network structure with respect to a given random graph model. The chapter starts with a discussion of the statistical significance of a given percentage of reciprocal edges in a directed graph and then introduces a new random graph model in which the degree sequence(s) are maintained. Finally, the notion of network motifs is introduced.

Keywords

Bipartite Graph Random Graph Common Neighbor Network Motif Degree Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag GmbH Austria 2016

Authors and Affiliations

  1. 1.TU Kaiserslautern, FB Computer ScienceGraph Theory and Analysis of Complex NetworksKaiserslauternGermany

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