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Authors: Narimene Dakiche 1 ; Karima Benatchba 1 ; Fatima Benbouzid-Si Tayeb 1 and Yahya Slimani 2

Affiliations: 1 Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole Nationale Supérieure d’Informatique (ESI), BP 68M - 16270 Oued Smar, Alger, Algeria ; 2 Computer Science Department, ISAMM Institute of Manouba, 2010 Manouba, Tunisia

Keyword(s): Dynamic Social Networks, Community Behavioral Events, Evolution Prediction.

Abstract: With its various real-life applications, predicting community evolution is a challenging task in the field of social network analysis. In this paper, we analyze communities’ evolution prediction accuracy in dynamic social networks. The proposed approach combines two key concepts of the process, aiming to enrich the prediction model by additional information that could improve the results: (1) a tailored network splitting that results in snapshots of different periods rather than a static one, and (2) the change rates of communities’ features that characterize them over time instead of absolute values of features. Our experiments on four real-world social networks confirm that community evolution prediction can be achieved with a very high accuracy by using both tailored network splitting as a first step of prediction process and change rates of features.

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Paper citation in several formats:
Dakiche, N.; Benatchba, K.; Benbouzid-Si Tayeb, F. and Slimani, Y. (2021). Impact of Tailored Network Splitting and Community Features’ Change Rates on Prediction Accuracy in Dynamic Social Networks. In Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST; ISBN 978-989-758-536-4; ISSN 2184-3252, SciTePress, pages 95-102. DOI: 10.5220/0010709300003058

@conference{webist21,
author={Narimene Dakiche. and Karima Benatchba. and Fatima {Benbouzid{-}Si Tayeb}. and Yahya Slimani.},
title={Impact of Tailored Network Splitting and Community Features’ Change Rates on Prediction Accuracy in Dynamic Social Networks},
booktitle={Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST},
year={2021},
pages={95-102},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010709300003058},
isbn={978-989-758-536-4},
issn={2184-3252},
}

TY - CONF

JO - Proceedings of the 17th International Conference on Web Information Systems and Technologies - WEBIST
TI - Impact of Tailored Network Splitting and Community Features’ Change Rates on Prediction Accuracy in Dynamic Social Networks
SN - 978-989-758-536-4
IS - 2184-3252
AU - Dakiche, N.
AU - Benatchba, K.
AU - Benbouzid-Si Tayeb, F.
AU - Slimani, Y.
PY - 2021
SP - 95
EP - 102
DO - 10.5220/0010709300003058
PB - SciTePress

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