  In this work, we study the problem of partitioning a set of graphs into
different groups such that the graphs in the same group are similar while the
graphs in different groups are dissimilar. This problem was rarely studied
previously, although there have been a lot of work on node clustering and graph
classification. The problem is challenging because it is difficult to measure
the similarity or distance between graphs. One feasible approach is using graph
kernels to compute a similarity matrix for the graphs and then performing
spectral clustering, but the effectiveness of existing graph kernels in
measuring the similarity between graphs is very limited. To solve the problem,
we propose a novel method called Deep Graph-Level Clustering (DGLC). DGLC
utilizes a graph isomorphism network to learn graph-level representations by
maximizing the mutual information between the representations of entire graphs
and substructures, under the regularization of a clustering module that ensures
discriminative representations via pseudo labels. DGLC achieves graph-level
representation learning and graph-level clustering in an end-to-end manner. The
experimental results on six benchmark datasets of graphs show that our DGLC has
state-of-the-art performance in comparison to many baselines.
