A unified framework for predicting attributes and links in social networks

Node attributes and their associated links are two crucial components of user profiling in social networks. Recently, several research works show that both attributes and links are partially predictable by various classification methods. However, most of these works suffer from an isolation of attributes and links. In this paper, we propose a novel unified framework to predict attributes and links simultaneously, by a two-layer artificial neural network that encodes them in the same network. We obtain a better predictive accuracy on both attributes and links than the previous state-of-the-art methods, tested on different real-world datasets. Our results show that we can discover much more useful information in the whole social networks than in node attributes or their associated links alone.