Social Network Embedding Method Basing Loop-free Path

Abstract

Network embedding’s goal is to learn low-dimensional feature representations of nodes in a network and use the learned features in various analysis tasks of the network, such as node classification, link prediction, community discovery and recommendation. Existing network embedding methods do not make sufficient use of higher-order structural information in social networks and are not ideal for application in social networks. A Loop-free Path Combined with Attributes Network Embedding (LFNE) method is proposed to solve these problems. The algorithm first calculates the node higher-order structure similarity based on the loop-free paths between nodes, eliminates the influence of loop paths and large degree nodes on node structure similarity, and enables the network embedding method to better fuse the higher-order structure information of social networks. The node structural similarity is finally applied to the low-dimensional feature representation of the learned nodes in the stacked denoising autoencoder. Experimental comparisons with representative algorithms in recent years on three social network datasets show that the LFNE algorithm can achieve relatively significant results in node classification and link prediction experiments, with better network embedding performance.