  Community detection (CD) is a classic graph inference task that partitions
nodes of a graph into densely connected groups. While many CD methods have been
proposed with either impressive quality or efficiency, balancing the two
aspects remains a challenge. This study explores the potential of deep graph
learning to achieve a better trade-off between the quality and efficiency of
K-agnostic CD, where the number of communities K is unknown. We propose PRoCD
(Pre-training & Refinement fOr Community Detection), a simple yet effective
method that reformulates K-agnostic CD as the binary node pair classification.
PRoCD follows a pre-training & refinement paradigm inspired by recent advances
in pre-training techniques. We first conduct the offline pre-training of PRoCD
on small synthetic graphs covering various topology properties. Based on the
inductive inference across graphs, we then generalize the pre-trained model
(with frozen parameters) to large real graphs and use the derived CD results as
the initialization of an existing efficient CD method (e.g., InfoMap) to
further refine the quality of CD results. In addition to benefiting from the
transfer ability regarding quality, the online generalization and refinement
can also help achieve high inference efficiency, since there is no
time-consuming model optimization. Experiments on public datasets with various
scales demonstrate that PRoCD can ensure higher efficiency in K-agnostic CD
without significant quality degradation.
