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.
|Number of pages||21|
|Journal||Networks and Heterogeneous Media|
|Publication status||Published - Sep 2012|
- Complex networks
- graph theory
- information content