Efficient Measurement of Complex Networks Using Link Queries

TL;DR

This paper investigates efficient methods for measuring complex networks using link queries, aiming to minimize costly tests while maximizing link discovery by leveraging properties of real-world networks.

Contribution

It introduces principles and strategies for network measurement based on network properties, and evaluates their efficiency and bias through experiments on real data.

Findings

Strategies reduce measurement costs significantly

Network properties can guide efficient link discovery

Bias varies with different measurement approaches

Abstract

Complex networks are at the core of an intense research activity. However, in most cases, intricate and costly measurement procedures are needed to explore their structure. In some cases, these measurements rely on link queries: given two nodes, it is possible to test the existence of a link between them. These tests may be costly, and thus minimizing their number while maximizing the number of discovered links is a key issue. This paper studies this problem: we observe that properties classically observed on real-world complex networks give hints for their efficient measurement; we derive simple principles and several measurement strategies based on this, and experimentally evaluate their efficiency on real-world cases. In order to do so, we introduce methods to evaluate the efficiency of strategies. We also explore the bias that different measurement strategies may induce.