Abstract: The purpose of graph clustering is to discover the community structure of the network.Aiming at the problem that the current clustering methods can not well obtain the deep potential community information of the network,and can not make sui-table information integration of the features,resulting in unclear semantics of the node community,a path-masked autoencoder guiding unsupervised attribute graph node clustering(PAUGC)model is proposed.This model utilizes an autoencoder to deeply dig the network topology structure by randomly masking network paths,thereby obtaining excellent global structural semantic information.Utilizing a normative method for information integration of the features,so that the node features are able to better characterize the class information of the features.In addition,the model combines modularity maximization to capture the under-lying community clusters information in the whole graph,aiming to more reasonably fuse it into the low-dimensional node features.Finally,the model iteratively optimizes and updates the clustering representation through self-training clustering to obtain the final node features.By conducting extensive experiments and comparisons with 11 classical methods on 8 benchmark datasets,PAUGC has been proven to be effective compared to current mainstream methods.

Key words: Deep graph clustering, Unsupervised learning, Feature integration, Module maximization, Self-training for clustering