Hier2vec: interpretable multi-granular representation learning for hierarchy in social networks

Abstract

Network representation learning (NRL) maps vertices into latent vector space for further network inference. The existing algorithms concern more about whether the vectors of two similar nodes be close in latent vector space while the hierarchy proximity has been largely neglected by them. The distribution of the representation vectors needs to reflect the hierarchical structural properties which widely exist in networks. In this paper, we propose a novel network representation learning framework that can encode the interpretable hierarchical structural semantics into the representation vectors. Specifically, we measure the distance and importance degree of nodes in the original network and map the nodes to a tree space. This makes the hierarchical structural relations in the original network be clearly revealed by the tree which is also of good interpretability. In this paper, the local structural proximities and the interpretable hierarchy knowledge are encoded into vector space by optimizing the objective function. Extensive experiments conducted on the realistic data sets demonstrate that the proposed approach outperforms the existing state-of-the-art approaches on tasks of node classification, link prediction, and visualization. Finally, a case study is conducted for further analysis about how the proposed model works.