Academic Collaborator Recommendation Based on Attributed Network Embedding

Purpose: Based on real-world academic data, this study aims to use network embedding technology to mining academic relationships, and investigate the effectiveness of the proposed embedding model on academic collaborator recommendation tasks. Design/methodology/approach: We propose an academic collaborator recommendation model based on attributed network embedding (ACR-ANE), which can get enhanced scholar embedding and take full advantage of the topological structure of the network and multi-type scholar attributes. The non-local neighbors for scholars are defined to capture strong relationships among scholars. A deep auto-encoder is adopted to encode the academic collaboration network structure and scholar attributes into a low-dimensional representation space. Findings: 1. The proposed non-local neighbors can better describe the relationships among scholars in the real world than the first-order neighbors. 2. It is important to consider the structure of the academic collaboration network and scholar attributes when recommending collaborators for scholars simultaneously. Research limitations: The designed method works for static networks, without taking account of the network dynamics. Practical implications: The designed model is embedded in academic collaboration network structure and scholarly attributes, which can be used to help scholars recommend potential collaborators. Originality/value: Experiments on two real-world scholarly datasets, Aminer and APS, show that our proposed method performs better than other baselines.