A unified network embedding algorithm for multi-type similarity measures

Traditional network embedding aims to learn representations by capturing a predefined vertex-to-vertex similarity measure. However, in practice, there are different types of similarity measures (e.g., connectivity and structural similarity), which are appropriate for different downstream applications. Meanwhile, it is hard to select the “best” similarity measure that can mostly benefit the application, considering the required domain knowledge of both application scenario and network science. It sometimes requires to cooperate these similarity measures with each other for achieving better performance. Therefore, automatically integrate multiple types of similarity measures into a uniform network embedding framework is critical to obtain effective vertex representations for a downstream application. In this paper, we address the above problem in social networks, and propose a semi-supervised representation learning algorithm. The general idea of our approach is to impose social influence, which occurs when one’s opinions, emotions, or behaviors are affected by others in a social network. Particularly, we build the connection between a user’s representation vector and the probability of her being influenced by another user to have a particular label (e.g., fraud, personal interest, etc.). We conduct efficient experiments based on six real-world datasets and find a clear improvement of our approach comparing with several state-of-the-art baselines.