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Randomized Spectral Co-Clustering for Large-Scale Directed Networks.
- Source :
-
Journal of Machine Learning Research . 2023, Vol. 24, p1-68. 68p. - Publication Year :
- 2023
-
Abstract
- Directed networks are broadly used to represent asymmetric relationships among units. Co-clustering aims to cluster the senders and receivers of directed networks simultaneously. In particular, the well-known spectral clustering algorithm could be modified as the spectral co-clustering to co-cluster directed networks. However, large-scale networks pose great computational challenges to it. In this paper, we leverage sketching techniques and derive two randomized spectral co-clustering algorithms, one random-projection-based and the other random-sampling-based, to accelerate the co-clustering of large-scale directed networks. We theoretically analyze the resulting algorithms under two generative models - the stochastic co-block model and the degree-corrected stochastic co-block model, and establish their approximation error rates and misclustering error rates, indicating better bounds than the state-of-the-art results of co-clustering literature. Numerically, we design and conduct simulations to support our theoretical results and test the efficiency of the algorithms on real networks with up to millions of nodes. A publicly available R package RandClust is developed for better usability and reproducibility of the proposed methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 15324435
- Volume :
- 24
- Database :
- Academic Search Index
- Journal :
- Journal of Machine Learning Research
- Publication Type :
- Academic Journal
- Accession number :
- 176355223