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

Sufen ZHAO; Rong PENG; Meng ZHANG; Liansheng TAN

doi:10.1587/transinf.2019EDP7108

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

Sufen ZHAO, Rong PENG, Meng ZHANG, Liansheng TAN

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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.

Publication: IEICE TRANSACTIONS on Information Vol.E103-D No.1 pp.71-84

Publication Date: 2020/01/01

Publicized: 2019/09/26

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2019EDP7108

Type of Manuscript: PAPER

Category: Fundamentals of Information Systems

Authors

Sufen ZHAO
  Wuhan University,Central China Normal University
Rong PENG
  Wuhan University
Meng ZHANG
  Central China Normal University
Liansheng TAN
  Central China Normal University,University of Tasmania

Keyword

heterogeneous networks, social networks, friend recommendation, co-author recommendation, random walk with restart

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Sufen ZHAO, Rong PENG, Meng ZHANG, Liansheng TAN, "HeteroRWR: A Novel Algorithm for Top-k Co-Author Recommendation with Fusion of Citation Networks" in IEICE TRANSACTIONS on Information, vol. E103-D, no. 1, pp. 71-84, January 2020, doi: 10.1587/transinf.2019EDP7108.
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 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2019EDP7108/_p

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@ARTICLE{e103-d_1_71,
author={Sufen ZHAO, Rong PENG, Meng ZHANG, Liansheng TAN, },
journal={IEICE TRANSACTIONS on Information},
title={HeteroRWR: A Novel Algorithm for Top-k Co-Author Recommendation with Fusion of Citation Networks},
year={2020},
volume={E103-D},
number={1},
pages={71-84},
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 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.},
keywords={},
doi={10.1587/transinf.2019EDP7108},
ISSN={1745-1361},
month={January},}

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TY - JOUR
TI - HeteroRWR: A Novel Algorithm for Top-k Co-Author Recommendation with Fusion of Citation Networks
T2 - IEICE TRANSACTIONS on Information
SP - 71
EP - 84
AU - Sufen ZHAO
AU - Rong PENG
AU - Meng ZHANG
AU - Liansheng TAN
PY - 2020
DO - 10.1587/transinf.2019EDP7108
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E103-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2020
AB - 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.
ER -