Parallel Clustering of Graphs for Anonymization and Recommender Systems

TL;DR

This paper introduces parallel graph clustering algorithms utilizing Monte Carlo and expectation maximization techniques within stochastic block models, aiming to improve recommender systems and social network anonymization.

Contribution

It presents novel parallel algorithms for graph clustering based on Monte Carlo and EM methods tailored for stochastic block models, applied to recommender systems and anonymization.

Findings

Algorithms outperform previous methods in clustering quality

Parallelization significantly reduces computation time

Effective in social network anonymization and recommender systems

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.