  The advisor-advisee relationship represents direct knowledge heritage, and
such relationship may not be readily available from academic libraries and
search engines. This work aims to discover advisor-advisee relationships hidden
behind scientific collaboration networks. For this purpose, we propose a novel
model based on Network Representation Learning (NRL), namely Shifu2, which
takes the collaboration network as input and the identified advisor-advisee
relationship as output. In contrast to existing NRL models, Shifu2 considers
not only the network structure but also the semantic information of nodes and
edges. Shifu2 encodes nodes and edges into low-dimensional vectors
respectively, both of which are then utilized to identify advisor-advisee
relationships. Experimental results illustrate improved stability and
effectiveness of the proposed model over state-of-the-art methods. In addition,
we generate a large-scale academic genealogy dataset by taking advantage of
Shifu2.
