Temporal networks have been successfully applied to analyse dynamics of networks. In this paper we focus on an approach recently introduced to identify dense subgraphs in a temporal network and we present a heuristic, based on the local search technique, for the problem. The experimental results we present on synthetic and real-world datasets show that our heuristic provides mostly better solutions (denser solutions) and that the heuristic is fast (comparable with the fastest method in literature, which is outperformed in terms of quality of the solutions). We present also experimental results of two variants of our method based on two diferent subroutines to compute a dense subgraph of a given graph.

(2021). Dense Sub-networks Discovery in Temporal Networks [journal article - articolo]. In SN COMPUTER SCIENCE. Retrieved from http://hdl.handle.net/10446/200862

Dense Sub-networks Discovery in Temporal Networks

Dondi, Riccardo;Hosseinzadeh, Mohammad Mehdi
2021

Abstract

Temporal networks have been successfully applied to analyse dynamics of networks. In this paper we focus on an approach recently introduced to identify dense subgraphs in a temporal network and we present a heuristic, based on the local search technique, for the problem. The experimental results we present on synthetic and real-world datasets show that our heuristic provides mostly better solutions (denser solutions) and that the heuristic is fast (comparable with the fastest method in literature, which is outperformed in terms of quality of the solutions). We present also experimental results of two variants of our method based on two diferent subroutines to compute a dense subgraph of a given graph.
articolo
Dondi, Riccardo; Hosseinzadeh, Mohammad Mehdi
(2021). Dense Sub-networks Discovery in Temporal Networks [journal article - articolo]. In SN COMPUTER SCIENCE. Retrieved from http://hdl.handle.net/10446/200862
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/200862
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