Learning Centrality by Learning to Route

Liav Bachar, Aviad Elyashar, Rami Puzis

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations


Developing a tailor-made centrality measure for a given task requires domain and network analysis expertise, as well as time and effort. Automatically learning arbitrary centrality measures provided ground truth node scores is an important research direction. In this article, we propose a generic deep learning architecture for centrality learning that relies on the insight that arbitrary centrality measures can be computed using Routing Betweenness Centrality (RBC) and our new differentiable implementation of RBC. The proposed Learned Routing Centrality (LRC) architecture optimizes the routing function of RBC to fit the ground truth scores. Results show that LRC can learn multiple types of centrality indices more accurately than state-of-the-art.

Original languageEnglish
Title of host publicationComplex Networks and Their Applications X - Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021
EditorsRosa Maria Benito, Chantal Cherifi, Hocine Cherifi, Esteban Moro, Luis M. Rocha, Marta Sales-Pardo
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9783030934088
StatePublished - 1 Jan 2022
Event10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 - Madrid, Spain
Duration: 30 Nov 20212 Dec 2021

Publication series

NameStudies in Computational Intelligence
ISSN (Print)1860-949X
ISSN (Electronic)1860-9503


Conference10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021

ASJC Scopus subject areas

  • Artificial Intelligence


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