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Social Network Analysis and Mining
Published in:

01-12-2016 | Original Article

Finding remarkably dense sequences of contacts in link streams

Authors: Noé Gaumont, Clémence Magnien, Matthieu Latapy

Published in: Social Network Analysis and Mining | Issue 1/2016

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Abstract

A link stream is a set of quadruplets (b, e, u, v) meaning that a link exists between u and v from time b to time e. Link streams model many real-world situations like contacts between individuals, connections between devices, and others. Much work is currently devoted to the generalization of classical graph and network concepts to link streams. We argue that the density is a valuable notion for understanding and characterizing links streams. We propose a method to capture specific groups of links that are structurally and temporally densely connected and show that they are meaningful for the description of link streams. To find such groups, we use classical graph community detection algorithms, and we assess obtained groups. We apply our method to several real-world contact traces (captured by sensors) and demonstrate the relevance of the obtained structures.
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Appendix
Available only for authorised users
Footnotes
1
Other community detection methods can be applied.
 
3
Candidates with one link represent 83 % of all candidates.
 
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Metadata
Title
Finding remarkably dense sequences of contacts in link streams
Authors
Noé Gaumont
Clémence Magnien
Matthieu Latapy
Publication date
01-12-2016
Publisher
Springer Vienna
Published in
Social Network Analysis and Mining / Issue 1/2016
Print ISSN: 1869-5450
Electronic ISSN: 1869-5469
DOI
https://doi.org/10.1007/s13278-016-0396-z

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