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Inferring Temporal Information from a Snapshot of a Dynamic Network.

Abstract：

:The problem of reverse-engineering the evolution of a dynamic network, known broadly as network archaeology, is one of profound importance in diverse application domains. In analysis of infection spread, it reveals the spatial and temporal processes underlying infection. In analysis of biomolecular interaction networks (e.g., protein interaction networks), it reveals early molecules that are known to be differentially implicated in diseases. In economic networks, it reveals flow of capital and associated actors. Beyond these recognized applications, it provides analytical substrates for novel studies - for instance, on the structural and functional evolution of the human brain connectome. In this paper, we model, formulate, and rigorously analyze the problem of inferring the arrival order of nodes in a dynamic network from a single snapshot. We derive limits on solutions to the problem, present methods that approach this limit, and demonstrate the methods on a range of applications, from inferring the evolution of the human brain connectome to conventional citation and social networks, where ground truth is known.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Sreedharan JK,Magner A,Grama A,Szpankowski W

doi

10.1038/s41598-019-38912-0

subject

Has Abstract

pub_date

2019-02-28 00:00:00

pages

3057

issue

1

issn

2045-2322

pii

10.1038/s41598-019-38912-0

journal_volume

9

pub_type

杂志文章

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