Dataset2Graph: A GNN-based Methodology for AutoML for Clustering

In this paper, we propose Dataset2Graph, a novel methodology that targets the problem of automated machine learning (AutoML) for clustering. Dataset2Graph offers an end-to-end solution to meta-learning, by encoding any input dataset as a similarity graph which undergoes graph reduction techniques (sparsification and coarsening) to obtain a smaller and more concise graph representation of the dataset. Besides making the processing more efficient, the graph reduction aims to keep the most important structural properties of the dataset. Subsequently, a graph neural network (GNN) architecture is applied on the reduced graph, producing a graph embedding, which is propagated to a meta-learner that processes this embedding to predict the best clustering algorithm for the dataset at hand. Our empirical evaluation using a set of 50 collected datasets shows the effectiveness of Dataset2Graph for different GNN models under various settings and in comparison with the state-of-the-art.