Deep attributed network representation learning via enhanced local attribute neighbor

Network representation learning aims to transform nodes in a network into low-dimensional spatial vectors while preserving the topological structure information of the network and its fundamental attributes, which has a wide range of practical applications. However, most existing attributed network representation learning methods only preserve part of the attributes and local or global topology information of the network, and do not fully capture the full attribute information of the complex interactions and the full topology information of the deep potential in the network. In the process of learning node embedding, it is a difficult and challenging task to fully and comprehensively capture and fuse the attribute and topology information in the network. To this end, we propose a new attributed network representation learning framework via enhanced local attribute neighbor, aiming to more effectively capture the global and local attribute information as well as the full topology information more comprehensively from the entire network. Specifically, a global attribute autoencoder is designed to model the mutual influence relationship of long-distance node attribute information, capture the global attribute neighbors of nodes from the whole network, and get the global attribute information of the complex interactions in the network. Additionally, a new random walk guide index, i.e., comprehensive influence, is designed to efficiently obtain the potential local and global topological structure information in the network. While at the same time, an enhanced local attribute neighbor skip-gram model is designed to obtain the local attribute information of nodes, so as to achieve the purpose of obtaining the network information in a full-aspect and multi-dimensional manner. We conduct extensive experiments on five real-world datasets for three downstream network analysis tasks: node classification, link prediction, and node clustering. The experimental results show that the method can achieve superior performance on each network analysis task, with the highest improvement of 3.94 % and 4.19 % in Micro-F1 and Macro-F1 over the optimal baseline method in node classification, respectively; 5.86 % and 5.2 % in Accuracy and Area Under Curve (AUC) in link prediction, respectively; and 7.73 %, 9.86 %, and 14.41 % in Normalized Mutual Information (NMI), Adjusted Rand Index (ARI), and Completeness (Comp) in clustering, respectively, which proves the effectiveness of the proposed method.