Abstract

Abstract. Many distributed systems lend themselves to be modelled as net-works, 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 quantify-ing 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 func-tional 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 net-work’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.