On congruity of nodes and assortative information content in complex networks

Mahendra Piraveenan*, Mikhail Prokopenko, Albert Y. Zomaya

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)


Many distributed systems lend themselves to be modelled as networks, where nodes can have a range of attributes and properties based on which they may be classified. In this paper, we attempt the task of quantifying varying levels of similarity among nodes in a complex network over a period of time. We analyze how this similarity varies as nodes implement their functional logic and node states vary accordingly. We then use information theory to analyze how much Shannon information is conveyed by such a similarity measure, and how such information varies with time. We also propose node congruity as a measure to quantify the contribution of each node to the network's scalar assortativity. Finally, focussing on networks with binary states, we present algorithms (logic functions) which can be implemented in nodes to maximize or minimize scalar assortativity in a given network, and analyze the corresponding tendencies in information content.

Original languageEnglish
Pages (from-to)441-461
Number of pages21
JournalNetworks and Heterogeneous Media
Issue number3
Publication statusPublished - Sep 2012
Externally publishedYes


  • Complex networks
  • graph theory
  • assortativity
  • information content
  • congruity


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