NDLI: Temporal distance metrics for social network analysis

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Temporal distance metrics for social network analysis

Content Provider	ACM Digital Library
Author	Mascolo, Cecilia Musolesi, Mirco Tang, John Latora, Vito
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.
Starting Page	31
Ending Page	36
Page Count	6
File Format	PDF
ISBN	9781605584454
DOI	10.1145/1592665.1592674
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-08-17
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Complex networks Temporal graphs Social networks Temporal metrics Information diffusion Temporal efficiency
Content Type	Text
Resource Type	Article

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