Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
This addresses the problem of uncovering direct knowledge heritage in academia, which is often unavailable in standard sources, though it appears incremental as it builds on existing NRL approaches.
The paper tackles the problem of discovering hidden advisor-advisee relationships in scientific collaboration networks by proposing Shifu2, a Network Representation Learning model that incorporates both network structure and semantic information, resulting in improved stability and effectiveness over state-of-the-art methods and generating a large-scale academic genealogy dataset.
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.