Temporal networks have been successfully applied to represent the dynamics of protein-protein interactions. In this paper we focus on the identification of dense subgraphs in temporal protein-protein interaction networks, a relevant problem to find group of proteins related to a given functionality. We consider a drawback of an existing approach for this problem that produce large time intervals over which temporal subgraphs are defined. We propose a problem to deal with this issue and we design (1) an exact algorithm based on dynamic programming which solves the problem in polynomial time and (2) a heuristic, based on a segmentation of the time domain and the computation of a refinement. The experimental results we present on seven protein-protein interaction networks show that in many cases our heuristic is able to reduce the time intervals with respect to those computed by the existing methods.

(2022). Dense Temporal Subgraphs in Protein-Protein Interaction Networks . Retrieved from https://hdl.handle.net/10446/234197

Dense Temporal Subgraphs in Protein-Protein Interaction Networks

Dondi, Riccardo;Hosseinzadeh, Mohammad Mehdi;
2022-01-01

Abstract

Temporal networks have been successfully applied to represent the dynamics of protein-protein interactions. In this paper we focus on the identification of dense subgraphs in temporal protein-protein interaction networks, a relevant problem to find group of proteins related to a given functionality. We consider a drawback of an existing approach for this problem that produce large time intervals over which temporal subgraphs are defined. We propose a problem to deal with this issue and we design (1) an exact algorithm based on dynamic programming which solves the problem in polynomial time and (2) a heuristic, based on a segmentation of the time domain and the computation of a refinement. The experimental results we present on seven protein-protein interaction networks show that in many cases our heuristic is able to reduce the time intervals with respect to those computed by the existing methods.
2022
Dondi, Riccardo; Hosseinzadeh, Mohammad Mehdi; Zoppis, Italo
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/234197
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