Effects of time window size and placement on the structure of an aggregated communication network

Gautier Krings*, Márton Karsai, Sebastian Bernhardsson, Vincent D. Blondel, Jari Saramäki

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

88 Citations (Scopus)
147 Downloads (Pure)

Abstract

Complex networks are often constructed by aggregating empirical data over time, such that a link represents the existence of interactions between the endpoint nodes and the link weight represents the intensity of such interactions within the aggregation time window. The resulting networks are then often considered static. More often than not, the aggregation time window is dictated by the availability of data, and the effects of its length on the resulting networks are rarely considered. Here, we address this question by studying the structural features of networks emerging from aggregating empirical data over different time intervals, focussing on networks derived from time-stamped, anonymized mobile telephone call records. Our results show that short aggregation intervals yield networks where strong links associated with dense clusters dominate; the seeds of such clusters or communities become already visible for intervals of around one week. The degree and weight distributions are seen to become stationary around a few days and a few weeks, respectively. An aggregation interval of around 30 days results in the stablest similar networks when consecutive windows are compared. For longer intervals, the effects of weak or random links become increasingly stronger, and the average degree of the network keeps growing even for intervals up to 180 days. The placement of the time window is also seen to affect the outcome: for short windows, different behavioural patterns play a role during weekends and weekdays, and for longer windows it is seen that networks aggregated during holiday periods are significantly different.

Original languageEnglish
Article number4
Pages (from-to)1-16
Number of pages16
JournalEPJ Data Science
Volume1
DOIs
Publication statusPublished - 2012
MoE publication typeA1 Journal article-refereed

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