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Authors: Silas P. Lima Filho ; Maria Claudia Cavalcanti and Claudia Marcela Justel

Affiliation: Military Institute of Engineering, Brazil

ISBN: 978-989-758-298-1

Keyword(s): Modelling Formalism, Link Prediction, Heuristics.

Related Ontology Subjects/Areas/Topics: Databases and Information Systems Integration ; Enterprise Information Systems ; Non-Relational Databases

Abstract: The importance of bringing the relational data to other models and technologies has been widely debated, as for example their representation as graphs. This model allows to perform topological analysis such as social analysis, link predictions or recommendations. There are already initiatives to map from a relational database to graph representation. However, they do not take into account the different ways to generate such graphs from data stored in relational databases, specially when the goal is to perform topological analysis. This work discusses how graph modeling alternatives from data stored in relational datasets may lead to useful results. However, this is not an easy task. The main contribution of this paper is towards managing such alternatives, taking into account that the graph model choice and the topological analysis to be used, depend on the links the user intends to predict. Experiments are reported and show interesting results, including modeling heuristics to guide the user on the graph model choice. (More)

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Paper citation in several formats:
P. Lima Filho, S.; Claudia Cavalcanti, M. and Marcela Justel, C. (2018). Managing Graph Modeling Alternatives for Link Prediction.In Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-758-298-1, pages 71-80. DOI: 10.5220/0006708700710080

@conference{iceis18,
author={Silas P. Lima Filho. and Maria Claudia Cavalcanti. and Claudia Marcela Justel.},
title={Managing Graph Modeling Alternatives for Link Prediction},
booktitle={Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2018},
pages={71-80},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006708700710080},
isbn={978-989-758-298-1},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Managing Graph Modeling Alternatives for Link Prediction
SN - 978-989-758-298-1
AU - P. Lima Filho, S.
AU - Claudia Cavalcanti, M.
AU - Marcela Justel, C.
PY - 2018
SP - 71
EP - 80
DO - 10.5220/0006708700710080

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