Two-state Markov-chain Poisson nature of individual cellphone call statistics

TL;DR

This study reveals that individual cellphone call activities are governed by two independent Poisson processes, leading to exponential distributions at the individual level, while population-level patterns exhibit power-law distributions due to superposition effects.

Contribution

It introduces a minimal two-state Markov chain model that accurately captures individual call activity distributions based on empirical data.

Findings

Individual call events show bursty patterns with alternating high and low activity periods.

At the individual level, the number of calls in both high and low activity periods follow exponential distributions.

Population-level call activity distributions exhibit power-law tails explained by superposition of individual exponential distributions.

Abstract

Humans are heterogenous and the behaviors of individuals could be different from that at the population level. We conduct an in-depth study of the temporal patterns of cellphone conversation activities of 73'339 anonymous cellphone users with the same truncated Weibull distribution of inter-call durations. We find that the individual call events exhibit a pattern of bursts, in which high activity periods are alternated with low activity periods. Surprisingly, the number of events in high activity periods are found to conform to a power-law distribution at the population level, but follow an exponential distribution at the individual level, which is a hallmark of absence of memory in individual call activities. Such exponential distribution is also observed for the number of events in low activity periods. Together with the exponential distributions of inter-call durations within bursts and of the intervals between consecutive bursts, we demonstrate that the individual call activities are driven by two independent Poisson processes, which can be combined within a minimal model in terms of a two-state first-order Markov chain giving very good agreement with the empirical distributions using the parameters estimated from real data for about half of the individuals in our sample. By measuring directly the distributions of call rates across the population, which exhibit power-law tails, we explain the difference with previous population level studies, purporting the existence of power-law distributions, via the "Superposition of Distributions" mechanism: The superposition of many exponential distributions of activities with a power-law distribution of their characteristic scales leads to a power-law distribution of the activities at the population level.