DeepDirect: Learning Directions of Social Ties with Edge-Based Network Embedding

Abstract

There is a lot of research work on social ties, few of which is about the directionality of social ties. However, the directionality is actually a basic but important attribute of social ties. In this paper, we present a supervised learning problem, the tie direction learning (TDL) problem, which aims to learn the directionality function of directed social networks. Two ways are introduced to solve the TDL problem: one is based on hand-crafted features and the other, named DeepDirect , learns the social tie representation through the topological information of the network. In DeepDirect , a novel network embedding approach, which directly maps the social ties to low-dimensional embedding vectors by deep learning techniques, is proposed. DeepDirect embeds the network considering three different aspects: preserving network topology, utilizing labeled data, and generating pseudo-labels based on observed directionality patterns. Two novel applications are proposed for the learned directionality function, i.e., direction discovery on undirected ties and direction quantification on bidirectional ties. Experiments are conducted on five different real-world data sets about these two tasks. The experimental results demonstrate our methods, especially DeepDirect , are effective and promising.