A Unified Framework for Information Consumption Based on Markov Chains

TL;DR

This paper introduces a Markov chain model as a comprehensive framework for understanding information consumption in complex networks, effectively capturing observed data patterns better than traditional power-law models.

Contribution

It presents the first Markov chain model that explains the formation of the 'trichotomy' in probability density functions in social and technical networks.

Findings

Model accurately fits real-world data

Outperforms classical power-law models

Explains the 'trichotomy' in data distributions

Abstract

This paper establishes a Markov chain model as a unified framework for understanding information consumption processes in complex networks, with clear implications to the Internet and big-data technologies. In particular, the proposed model is the first one to address the formation mechanism of the "trichotomy" in observed probability density functions from empirical data of various social and technical networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical power-law models.