论文标题
使用动态扩张的聚集在深度剩余GCN中重叠的社区检测
Overlapping Community Detection using Dynamic Dilated Aggregation in Deep Residual GCN
论文作者
论文摘要
重叠的社区检测是图挖掘中的关键问题。一些研究考虑使用图形卷积网络（GCN）来解决该问题。但是，在一般不规则图的情况下，合并深图卷积网络仍然具有挑战性。在这项研究中，我们根据我们新颖的动态扩张的聚合机制和一个统一的基于端到端的编码器框架设计了一个深层动态残留图卷积网络（DynaresGCN），以检测网络中的重叠群落。 Deep DynaresGCN模型用作编码器，而我们将Bernoulli-Poisson（BP）模型合并为解码器。因此，我们将重叠的社区检测框架应用于研究主题数据集中，而没有地面真相，Facebook的一组网络具有可靠的（手工标记）地面真相，以及具有具有经验（未手动标记）地面真相的一组非常大的合着者网络。我们在这些数据集上进行的实验比许多最先进的方法表明，用于检测网络中重叠社区的最先进方法。
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.
