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Ranking influential nodes in networks from aggregate local information

Bartolucci, Silvia, Caccioli, Fabio, Caravelli, Francesco and Vivo, Pierpaolo (2023) Ranking influential nodes in networks from aggregate local information. Physical Review Research, 5 (3). ISSN 2643-1564

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Identification Number: 10.1103/PhysRevResearch.5.033123


Many complex systems exhibit a natural hierarchy in which elements can be ranked according to a notion of “influence”. While the complete and accurate knowledge of the interactions between constituents is ordinarily required for the computation of nodes' influence, using a low-rank approximation we show that—in a variety of contexts—local and aggregate information about the neighborhoods of nodes is enough to reliably estimate how influential they are without the need to infer or reconstruct the whole map of interactions. Our framework is successful in approximating with high accuracy different incarnations of influence in systems as diverse as the WWW PageRank, trophic levels of ecosystems, upstreamness of industrial sectors in complex economies, and centrality measures of social networks, as long as the underlying network is not exceedingly sparse. We also discuss the implications of this “emerging locality” on the approximate calculation of nonlinear network observables.

Item Type: Article
Additional Information: © 2023 The Author(s)
Divisions: LSE
Systemic Risk Centre
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 03 Oct 2023 15:09
Last Modified: 16 Jun 2024 19:18

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