Temporal Distance Metrics for Social Network Analysis

John Tang, Mirco Musolesi, Cecilia Mascolo, Vito Latora

Research output: Chapter in Book/Report/Conference proceedingConference contribution

139 Citations (Scopus)

Abstract

The analysis of social and technological networks has attracted a lot of attention as social networking applications and mobile sensing devices have given us a wealth of real data. Classic studies looked at analysing static or aggregated networks, i.e., networks that do not change over time or built as the results of aggregation of information over a certain period of time. Given the soaring collections of measurements related to very large, real network traces, researchers are quickly starting to realise that connections are inherently varying over time and exhibit more dimensionality than static analysis can capture.

In this paper we propose new temporal distance metrics to quantify and compare the speed (delay) of information diffusion processes taking into account the evolution of a network from a local and global view. We show how these metrics are able to capture the temporal characteristics of time-varying graphs, such as delay, duration and time order of contacts (interactions), compared to the metrics used in the past on static graphs. As a proof of concept we apply these techniques to two classes of time-varying networks, namely connectivity of mobile devices and e-mail exchanges.

Original languageEnglish
Title of host publicationProceedings of the 2ND ACM SIGCOMM Workshop on Online Social Networks (WOSN '09)
Place of PublicationNew York
PublisherACM
Pages31-36
Number of pages6
ISBN (Print)978-1-60558-445-4
Publication statusPublished - 2009

Keywords

  • Temporal Graphs
  • Temporal Metrics
  • Temporal Efficiency
  • Social Networks
  • Complex Networks
  • Information Diffusion

Fingerprint

Dive into the research topics of 'Temporal Distance Metrics for Social Network Analysis'. Together they form a unique fingerprint.

Cite this