HSEM: highly scalable node embedding for link prediction in very large-scale social networks

Abstract

Very large-scale social networks are typically sparse and dynamic and often have millions of nodes and billions of links. Link prediction in very large-scale networks is a challenging task for most existing methods. This paper investigates the link prediction problem in very large-scale networks. We propose a model, namely, a very large-scale network co-occurrence embedding algorithm (Hsem), which learns the co-occurrence features of node pairs to embed nodes into a vector with a lower and fixed dimension. A damping-based random walk algorithm is also developed to extract the hierarchical structural information of node pairs. Moreover, we design an incremental learning algorithm based on Hsem to meet the requirements of highly dynamic evolution in very large-scale networks, which significantly improves learning speed while maintaining accuracy. Finally, we present controlled experiments employing the proposed and baseline methods on eight challenging real-world large-scale datasets with a fixed dropout percentage and calculate the time overhead and receiver operating characteristics. The results show that Hsem performs comparably to current state-of-the-art methods and consistently outperforms the baselines in different experimental settings.