@inproceedings{17cfb8d51e924776a3c75f4c114e6a55,
title = "Learning Centrality by Learning to Route",
abstract = "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.",
author = "Liav Bachar and Aviad Elyashar and Rami Puzis",
note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 10th International Conference on Complex Networks and Their Applications, COMPLEX NETWORKS 2021 ; Conference date: 30-11-2021 Through 02-12-2021",
year = "2022",
month = jan,
day = "1",
doi = "10.1007/978-3-030-93409-5_21",
language = "American English",
isbn = "9783030934088",
series = "Studies in Computational Intelligence",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "247--259",
editor = "Benito, {Rosa Maria} and Chantal Cherifi and Hocine Cherifi and Esteban Moro and Rocha, {Luis M.} and Marta Sales-Pardo",
booktitle = "Complex Networks and Their Applications X - Proceedings of the 10th International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2021",
address = "Germany",
}