Parallel Clustering of Graphs for Anonymization and Recommender Systems
Data Structures and Algorithms
2016-09-05 v2 Information Retrieval
Social and Information Networks
Abstract
Graph clustering is widely used in many data analysis applications. In this paper we propose several parallel graph clustering algorithms based on Monte Carlo simulations and expectation maximization in the context of stochastic block models. We apply those algorithms to the specific problems of recommender systems and social network anonymization. We compare the experimental results to previous propositions.
Cite
@article{arxiv.1609.00161,
title = {Parallel Clustering of Graphs for Anonymization and Recommender Systems},
author = {Frederic Prost and Jisang Yoon},
journal= {arXiv preprint arXiv:1609.00161},
year = {2016}
}
Comments
submitted to VLDB'17