HeteroRWR: A Novel Algorithm for Top-k Co-Author Recommendation with Fusion of Citation Networks

Sufen ZHAO  Rong PENG  Meng ZHANG  Liansheng TAN  

Publication
IEICE TRANSACTIONS on Information and Systems   Vol.E103-D   No.1   pp.71-84
Publication Date: 2020/01/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2019EDP7108
Type of Manuscript: PAPER
Category: Fundamentals of Information Systems
Keyword: 
heterogeneous networks,  social networks,  friend recommendation,  co-author recommendation,  random walk with restart,  

Full Text: PDF(1MB)>>
Buy this Article




Summary: 
It is of great importance to recommend collaborators for scholars in academic social networks, which can benefit more scientific research results. Facing the problem of data sparsity of co-author recommendation in academic social networks, a novel recommendation algorithm named HeteroRWR (Heterogeneous Random Walk with Restart) is proposed. Different from the basic Random Walk with Restart (RWR) model which only walks in homogeneous networks, HeteroRWR implements multiple random walks in a heterogeneous network which integrates a citation network and a co-authorship network to mine the k mostly valuable co-authors for target users. By introducing the citation network, HeteroRWR algorithm can find more suitable candidate authors when the co-authorship network is extremely sparse. Candidate recommenders will not only have high topic similarities with target users, but also have good community centralities. Analyses on the convergence and time efficiency of the proposed approach are presented. Extensive experiments have been conducted on DBLP and CiteSeerX datasets. Experimental results demonstrate that HeteroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.