BOGK: Bayesian Optimization-Driven Graph Kernel Ensemble for Graph-Level Clustering

Graph-level clustering aims to partition a set of graphs into different clusters and has important applications in social networks, bioinformatics, etc. Although there have been some approaches to graph-level clustering such as various graph kernels and graph neural networks, it remains a huge challenge to select kernels and neural network architectures, since the task is unsupervised. Moreover, the clustering accuracy and model interpretability of these approaches are low and should be improved to satisfy practical needs. To address these issues, in this work, we propose a graph-level clustering method that uses Bayesian optimization to integrate various graph kernels (BOGK). BOGK aggregates the similarity matrices generated by different graph kernels and automatically learns the aggregation weights and a thresholding parameter via maximizing internal cluster validity indices. Our BOGK is free of manual hyperparameter tuning via Bayesian optimization, while it enjoys considerable interpretability, as the weight for each similarity matrix represents the importance of different structural or pattern information in graphs. Experimental results show that our BOGK outperforms the state-of-the-art on ten graph benchmark datasets.