A random graph generation algorithm for the analysis of social networks

Social network analysis (SNA) is a rapidly growing field with numerous applications in industry and government. However, the field still lacks means to generate random social networks with certain desired properties, thus inhibiting their ability to test new SNA algorithms and metrics. Available ran...

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Veröffentlicht in:Journal of defense modeling and simulation 2014-07, Vol.11 (3), p.265-276
Hauptverfasser: Morris, James F., O’Neal, Jerome W., Deckro, Richard F.
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container_title Journal of defense modeling and simulation
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creator Morris, James F.
O’Neal, Jerome W.
Deckro, Richard F.
description Social network analysis (SNA) is a rapidly growing field with numerous applications in industry and government. However, the field still lacks means to generate random social networks with certain desired properties, thus inhibiting their ability to test new SNA algorithms and metrics. Available random graph generation algorithms suffer from tendencies to generate disconnected graphs and sometimes induce undesirable network properties. In this paper, we present an algorithm, the prescribed node degree, connected graph (PNDCG) algorithm, designed to generate weakly connected social networks. Extensions to the PNDCG algorithm allow one to create random graphs that control the clustering coefficient and degree correlation within the generated networks. Empirical test results demonstrate the capability of the PNDCG algorithm to produce networks with the desired properties.
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subjects Algorithmics. Computability. Computer arithmetics
Applied sciences
Computer science
control theory
systems
Computer systems and distributed systems. User interface
Data processing. List processing. Character string processing
Exact sciences and technology
Information retrieval. Graph
Memory organisation. Data processing
Software
Theoretical computing
title A random graph generation algorithm for the analysis of social networks
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