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.

翻译：重叠社区检测是图挖掘中的一个关键问题。已有研究尝试利用图卷积网络（GCN）解决该问题，但在处理通用不规则图时，仍难以实现深度图卷积网络的应用。本研究基于我们提出的新型动态膨胀聚合机制与统一的端到端编码器-解码器框架，设计了一种深度动态残差图卷积网络（DynaResGCN），用于检测网络中的重叠社区。深度DynaResGCN模型作为编码器，而我们将伯努利-泊松（BP）模型作为解码器。最终，我们将该重叠社区检测框架应用于无真实标签的研究主题数据集、具有可靠（人工标注）真实标签的Facebook网络数据集，以及具有经验性（非人工标注）真实标签的超大规模合著网络数据集。实验结果表明，该方法在多个数据集上的重叠社区检测性能显著优于当前众多先进方法。