Fast Cluster Detection in Networks by First Order Optimization
By Immanuel M. Bomze, Francesco Rinaldi, Damiano Zeffiro. 2021
10 citations recorded by the source corpus.
Cluster detection plays a fundamental role in the analysis of data. In this paper, we focus on the use of s-defective clique models for network-based cluster detection and propose a nonlinear optimization approach that efficiently handles those models in practice. In particular, we introduce an equivalent continuous formulation for the problem under analysis, and we analyze some tailored variants of the Frank-Wolfe algorithm that enable us to quickly find maximal s-defective cliques. The good practical behavior of those algorithmic tools, which is closely connected to their support identification properties, makes them very appealing in practical applications. The reported numerical results clearly show the effectiveness of the proposed approach.
Subfields: Computational Mathematics, Computational Mechanics, Statistics and Probability, Graph Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computational Mathematics
In Computational Mechanics
- Thomas Kerdreux
- Damien Scieur
- Simon Lacoste-Julien
- Lewis Liu
- Peter Richtárik
- Fabian Pedregosa
- Sebastian Pokutta
- Zeyuan Allen-Zhu
In Statistics and Probability
In Graph Learning
Citations in the corpus
- Affine Invariant Analysis of Frank-Wolfe on Strongly Convex Sets (2020, background): “(Braun et al., 2017; Kerdreux et al., 2018; Braun et al., 2019; Combettes & Pokutta, 2020; Carderera & Pokutta, 2020; Mortagy et al., 2020; Combettes et al., 2020; Bomze et al., 2021)) and in their theoretical understanding.”