The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
학술 소셜 네트워크에서 학자들에게 협력자를 추천하는 것은 매우 중요하며, 이는 보다 과학적인 연구 결과에 도움이 될 수 있습니다. 학술 소셜 네트워크에서 공동 저자 추천의 데이터 희박 문제에 직면하여 HeteroRWR(Heterogeneous Random Walk with Restart)이라는 새로운 추천 알고리즘이 제안되었습니다. 동종 네트워크에서만 탐색하는 기본 RWR(Random Walk with Restart) 모델과 달리, HeteroRWR은 인용 네트워크와 공동저자 네트워크를 통합하는 이종 네트워크에서 여러 무작위 탐색을 구현하여 k 대상 사용자에게 가장 귀중한 공동 저자입니다. 인용 네트워크를 도입함으로써 HeteroRWR 알고리즘은 공동 저자 네트워크가 매우 희박할 때 더 적합한 후보 저자를 찾을 수 있습니다. 후보 추천자는 대상 사용자와 높은 주제 유사성을 가질 뿐만 아니라 커뮤니티 중심성도 우수합니다. 제안된 접근법의 수렴 및 시간 효율성에 대한 분석이 제시됩니다. DBLP 및 CiteSeerX 데이터 세트에 대해 광범위한 실험이 수행되었습니다. 실험 결과는 불완전한 인용 데이터 세트를 통합하는 경우에도 HeteroRWR이 정밀도 및 재현율 측면에서 최첨단 기준 방법보다 성능이 우수하다는 것을 보여줍니다.
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
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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},}
부
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 -