Augmented label propagation for seed set expansion

Abstract

In many applications such as social network analysis and recommendation systems, it is of particular interest to identify a group of similar nodes/users/items. However, in networks of massive size, manual labeling process becomes intractable. A practical means is to mark a small number of nodes as seeds, and then expand them to the rest (unlabeled) ones, which is also known as seed set expansion. We present a novel method for seed set expansion by leveraging information spreading dynamics through label propagation. In particular, by devising an augmented, community-based label propagation, we can fully exploit the information of the limited seed nodes, and apply the connectivity structure of the whole network in imposing a larger number of constraints on the label propagation process, thus achieving an improved estimation. Our method can increase the effective number of seed nodes in that it can achieve a better estimation than other propagation methods using the same number of seeds. Extensive experiments on real-world datasets demonstrate the effectiveness and adaptiveness of our method, compared to the state-of-the-art approaches.