@inproceedings{9e719032509e4031a4463610119ce3bd,
title = "Agent-Based Vector-Label Propagation for Explaining Social Network Structures",
abstract = "Even though Social Network Analysis is quite helpful in studying the structural properties of interconnected systems, real-world networks reveal much more hidden characteristics from interacting domain-specific features. In this study, we designed an Agent-based Vector-label PRopagation Algorithm (AVPRA), which captures both structural properties and domain-specific features of a given network by assigning vectors of features to constituting agents. Experimental analysis proves that our algorithm is accurate in revealing the structural properties of a network in an explainable fashion. Furthermore, the resulting vector-labels are suitable for downstream machine learning tasks.",
keywords = "Explainability, Social network analysis, Vector-label propagation",
author = "Valerio Bellandi and Paolo Ceravolo and Ernesto Damiani and Samira Maghool",
note = "Funding Information: Acknowledgements. This work was supported by the Universit{\`a} degli Studi di Milano under the Seal of Excellence (SoE) SEED 2020 Project POPULITE - POPUlist Language in ITalian political Elites (Project ID 1090). Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 16th International Conference on Knowledge Management in Organisations, KMO 2022 ; Conference date: 11-07-2022 Through 14-07-2022",
year = "2022",
doi = "10.1007/978-3-031-07920-7_24",
language = "British English",
isbn = "9783031079191",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "306--317",
editor = "Lorna Uden and I-Hsien Ting and Birgit Feldmann",
booktitle = "Knowledge Management in Organisations - 16th International Conference, KMO 2022, Proceedings",
address = "Germany",
}