Differentiable parameter-less co-clustering using graph neural networks

Abstract Co-clustering refers to the simultaneous clustering of rows and columns in a data matrix, uncovering joint patterns between two distinct sets, such as documents and terms or users and products. Traditional co-clustering algorithms typically rely on discrete optimization techniques based on enumeration, which can limit both scalability and flexibility. In this paper, we introduce a differentiable programming approach to co-clustering that enables the continuous optimization of co-partitions using graph neural networks. Our method is grounded in an associative co-clustering quality measure that is independent of the number of clusters and dynamically adjusts this parameter by jointly considering both partitions. By leveraging automatic differentiation and graph neural networks, our approach scales to very large datasets while maintaining high-quality co-cluster structures. We evaluate our method using different types of graph neural networks and initialization strategies. Furthermore, when compared with recent state-of-the-art methods for co-clustering and graph clustering, our approach achieves competitive or superior results in terms of accuracy. Most importantly, it is the only algorithm that successfully completes on the largest benchmark dataset.