Network Performance Rank: An Approach for Comparison of Complex Networks

TL;DR

This paper introduces a new method to compare complex networks by ranking them based on their performance metrics using a decision analysis approach, specifically employing TOPSIS, to distinguish networks effectively.

Contribution

The paper presents a novel ranking approach for complex networks using TOPSIS and a statistical method to evaluate network performance based on event frequency analysis.

Findings

The method effectively discriminates between networks based on node event occurrences.

Experiments on synthetic networks demonstrate the approach's feasibility.

The ranking technique is computationally efficient.

Abstract

Researchers have typically concentrated on analyzing what happens internally in a complex network and using this to distinguish between nodes. However, there has been less effort towards comparing between different networks. In this paper, we proposed a novel approach to rank alternative complex networks based on their performances. We consider this as a ranking problem in decision analysis based on occurring positive/negative frequent events as criteria, and using the TOPSIS method to rank alternatives. In order to assign a score to the networks for each criterion, a statistical method that estimates the expected value of positive/negative frequent events on a random node is presented. The proposed technique is efficient in terms of algorithm complexity and is capable of discriminating events occurring between important nodes over those between less significant nodes. The experiments, conducted on several synthetic networks, demonstrate the feasibility and applicability of the ranking methodology.