Modeling and verifying a broad array of network properties

TL;DR

This paper introduces a network growth model based on sequential attachment of linked node groups, analyzing how tuning parameters affects network properties and demonstrating its ability to replicate diverse real-world network characteristics.

Contribution

It presents a novel graphlet-based network growth model and a comprehensive attribute vector for network comparison, showing improved coverage of real-world network properties.

Findings

Tuning attachment probability alpha controls degree distribution and assortativity.

The model achieves a wide range of properties matching real-world networks.

Extended model covers more real-world network features than classic models.

Abstract

Motivated by widely observed examples in nature, society and software, where groups of already related nodes arrive together and attach to an existing network, we consider network growth via sequential attachment of linked node groups, or graphlets. We analyze the simplest case, attachment of the three node V-graphlet, where, with probability alpha, we attach a peripheral node of the graphlet, and with probability (1-alpha), we attach the central node. Our analytical results and simulations show that tuning alpha produces a wide range in degree distribution and degree assortativity, achieving assortativity values that capture a diverse set of many real-world systems. We introduce a fifteen-dimensional attribute vector derived from seven well-known network properties, which enables comprehensive comparison between any two networks. Principal Component Analysis (PCA) of this attribute vector space shows a significantly larger coverage potential of real-world network properties by a simple extension of the above model when compared against a classic model of network growth.