Attributed Network Embedding Based on Attributed-Subgraph-Based Random Walk for Community Detection

The random-walk-based attribute network embedding methods aim to learn a low-dimensional embedding vector for each node considering the network structure and node attributes, facilitating various downstream inference tasks. However, most existing attribute network embedding methods base on random walk usually sample many redundant samples and suffer from inconsistency between node structure and attributes. In this paper, we propose a novel attributed network embedding method for community detection, which can generate node sequences based on attributed-subgraph-based random walk and filter redundant samples before model training. In addition, an improved network embedding enhancement strategy is applied to integrate high-order attributed and structure information of nodes into embedding vectors. Experimental results of community detection on synthetic network and real-world network show that our algorithm is effective and efficient compared with other algorithms.