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

Abstract

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 coauthors 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 Het-eroRWR outperforms state-of-the-art baseline methods in terms of precision and recall rate even in the case of incorporating an incomplete citation dataset.

Proximity‐aware research leadership recommendation in research collaboration via deep neural networks

“…A considerable effort has been dedicated to facilitating various types of collaborator recommendation approaches. Generally, these approaches can be divided into three main categories (Pradhan & Pal, 2020; Zhao et al, 2020): network‐based models, content‐based models, and hybrid models. The network‐based models incorporate network features.…”

Discovering the role model of authors by embedding research history

“…It can provide more comprehensive and detailed information regarding the network structure and meaning than the co-authorship networks. Therefore, the various methods [25,26] have been proposed for recommending co-authors using the meta-path in heterogeneous bibliographic networks.…”