Parallel Clustering of Graphs for Anonymization and Recommender Systems
This work addresses graph clustering for social network anonymization and recommender systems, but appears incremental as it builds on existing stochastic block models.
The authors tackled graph clustering for anonymization and recommender systems by proposing parallel algorithms based on Monte Carlo simulations and expectation maximization, comparing results to previous methods.
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.