  Overlapping community detection is a key problem in graph mining. Some
research has considered applying graph convolutional networks (GCN) to tackle
the problem. However, it is still challenging to incorporate deep graph
convolutional networks in the case of general irregular graphs. In this study,
we design a deep dynamic residual graph convolutional network (DynaResGCN)
based on our novel dynamic dilated aggregation mechanisms and a unified
end-to-end encoder-decoder-based framework to detect overlapping communities in
networks. The deep DynaResGCN model is used as the encoder, whereas we
incorporate the Bernoulli-Poisson (BP) model as the decoder. Consequently, we
apply our overlapping community detection framework in a research topics
dataset without having ground truth, a set of networks from Facebook having a
reliable (hand-labeled) ground truth, and in a set of very large co-authorship
networks having empirical (not hand-labeled) ground truth. Our experimentation
on these datasets shows significantly superior performance over many
state-of-the-art methods for the detection of overlapping communities in
networks.
