Dynamic Computation of Network Statistics via Updating Schema

TL;DR

This paper introduces an efficient updating scheme for calculating key network statistics, enabling faster analysis of evolving large networks and aiding in predicting their future changes.

Contribution

The paper presents a novel updating method for network statistics that reduces computational effort and helps in modeling network evolution.

Findings

Significantly faster computation of network statistics during network evolution

Ability to identify edges/nodes causing extremal changes in network metrics

Potential for predicting and designing network evolution rules

Abstract

In this paper we derive an updating scheme for calculating some important network statistics such as degree, clustering coefficient, etc., aiming at reduce the amount of computation needed to track the evolving behavior of large networks; and more importantly, to provide efficient methods for potential use of modeling the evolution of networks. Using the updating scheme, the network statistics can be computed and updated easily and much faster than re-calculating each time for large evolving networks. The update formula can also be used to determine which edge/node will lead to the extremal change of network statistics, providing a way of predicting or designing evolution rule of networks.