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Title: Dynamic graphs, community detection, and Riemannian geometry

Journal Article · · Applied Network Science
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A community is a subset of a wider network where the members of that subset are more strongly connected to each other than they are to the rest of the network. In this paper, we consider the problem of identifying and tracking communities in graphs that change over time {dynamic community detection} and present a framework based on Riemannian geometry to aid in this task. Our framework currently supports several important operations such as interpolating between and averaging over graph snapshots. We compare these Riemannian methods with entry-wise linear interpolation and that the Riemannian methods are generally better suited to dynamic community detection. Next steps with the Riemannian framework include developing higher-order interpolation methods (e.g. the analogues of polynomial and spline interpolation) and a Riemannian least-squares regression method for working with noisy data.

Research Organization:
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
Sponsoring Organization:
USDOE
DOE Contract Number:
AC05-76RL01830
OSTI ID:
1434842
Report Number(s):
PNNL-SA-129998
Journal Information:
Applied Network Science, Vol. 3, Issue 1; ISSN 2364-8228
Country of Publication:
United States
Language:
English

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Related Subjects

dynamic community detection
Riemannian geometry