Is the simple assignment enough? Exploring the interpretability for community detection

摘要

The maximum likelihood estimation is a probabilistic inferencing model of community connectivity in large networks. In general, only the adjacency matrix is utilized to perform community structure parameter inference. Although there are recent examples that combine connectivity and attribute information for community detection, our model is an enhanced overlapping community detection model that combines adjacency spectral embedding with maximum likelihood estimation. This provides the flexibility of complex networks to increase connectivity information through measurements from attribute embedding. The attribute information can be effectively captured and transformed by attribute embedding to encode the combination with structure information. Then, the link strength among communities is designed to adjust the impact of these structural information on community generation based on the contribution of the structure to the clusters, and the node assignment allow for the nature of the real network (overlapping and outliers). Experiments highlight attributed networks in which attributed community detection task provides satisfactory performance.