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Authors: Hadjer Djahnit and Malika Bessedik

Affiliation: Laboratoire des Méthodes de Conception de Systèmes (LMCS), Ecole Nationale Supérieure d’Informatique (ESI), BP 68M -16 270 Oued Smar, Alger, Algeria

Keyword(s): Social Network Analysis, Data Mining, Graph Mining, Frequent Conceptual Links, Frequent Itemset Mining.

Abstract: In the domain of social network analysis, the frequent pattern mining task gives large opportunities for knowledge discovery. One of the most recent variations of the pattern definition applied to social networks is the frequent conceptual links (FCL). A conceptual link represents a set of links connecting groups of nodes such as nodes of each group share common attributes. When the number of these links exceeds a predefined threshold, it is referred to as a frequent conceptual link and it aims to describe the network in term of the most connected type of nodes while exploiting structural and semantic information of the network. Since the inception of this technique, a number of improvements were achieved in the search process in order to optimise its performances. In this paper, we propose a new algorithm for extracting frequent conceptual links from large networks. By adopting a new compressed structure for the network, the proposed approach reaches up to 90% of gain in the executi on time. (More)

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Paper citation in several formats:
Djahnit, H. and Bessedik, M. (2021). Exhaustive Solution for Mining Frequent Conceptual Links in Large Networks using a Binary Compressed Representation. In Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR; ISBN 978-989-758-533-3; ISSN 2184-3228, SciTePress, pages 180-190. DOI: 10.5220/0010654800003064

@conference{kdir21,
author={Hadjer Djahnit. and Malika Bessedik.},
title={Exhaustive Solution for Mining Frequent Conceptual Links in Large Networks using a Binary Compressed Representation},
booktitle={Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR},
year={2021},
pages={180-190},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010654800003064},
isbn={978-989-758-533-3},
issn={2184-3228},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2021) - KDIR
TI - Exhaustive Solution for Mining Frequent Conceptual Links in Large Networks using a Binary Compressed Representation
SN - 978-989-758-533-3
IS - 2184-3228
AU - Djahnit, H.
AU - Bessedik, M.
PY - 2021
SP - 180
EP - 190
DO - 10.5220/0010654800003064
PB - SciTePress

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