Graph Embedding for Scholar Recommendation in Academic Social Networks

The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommen...

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Autores principales:	Chengzhe Yuan, Yi He, Ronghua Lin, Yong Tang
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	academic social networks recommendation system scholarly data graph embedding academic features Physics QC1-999
Acceso en línea:	https://doaj.org/article/bea4b9189c544d80b9be161905e413c9
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id	oai:doaj.org-article:bea4b9189c544d80b9be161905e413c9
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spelling	oai:doaj.org-article:bea4b9189c544d80b9be161905e413c92021-12-01T10:49:33ZGraph Embedding for Scholar Recommendation in Academic Social Networks2296-424X10.3389/fphy.2021.768006https://doaj.org/article/bea4b9189c544d80b9be161905e413c92021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fphy.2021.768006/fullhttps://doaj.org/toc/2296-424XThe academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates) and various relationships (e.g., collaboration relationship between team members, citations, and co-authorships), which forms a complex heterogeneous academic network. Our goal is to recommend potential similar scholars for users in ASNs. In this article, we propose to design a graph embedding-based scholar recommendation system by leveraging academic auxiliary information. First, we construct enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by a graph embedding framework, which helps the system measure the similarity between scholars based on their representation vectors. Finally, the system generates potential similar scholars for users in ASNs for the final recommendation. We evaluate the effectiveness of our model on five real-world datasets: SCHOLAT, Zhihu, APS, Yelp and Gowalla. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.Chengzhe YuanYi HeRonghua LinYong TangFrontiers Media S.A.articleacademic social networksrecommendation systemscholarly datagraph embeddingacademic featuresPhysicsQC1-999ENFrontiers in Physics, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	academic social networks recommendation system scholarly data graph embedding academic features Physics QC1-999
spellingShingle	academic social networks recommendation system scholarly data graph embedding academic features Physics QC1-999 Chengzhe Yuan Yi He Ronghua Lin Yong Tang Graph Embedding for Scholar Recommendation in Academic Social Networks
description	The academic social networks (ASNs) play an important role in promoting scientific collaboration and innovation in academic society. Accompanying the tremendous growth of scholarly big data, finding suitable scholars on ASNs for collaboration has become more difficult. Different from friend recommendation in conventional social networks, scholar recommendation in ASNs usually involves different academic entities (e.g., scholars, scientific publications, and status updates) and various relationships (e.g., collaboration relationship between team members, citations, and co-authorships), which forms a complex heterogeneous academic network. Our goal is to recommend potential similar scholars for users in ASNs. In this article, we propose to design a graph embedding-based scholar recommendation system by leveraging academic auxiliary information. First, we construct enhanced ASNs by integrating two types of academic features extracted from scholars’ academic information with original network topology. Then, the refined feature representations of the scholars are obtained by a graph embedding framework, which helps the system measure the similarity between scholars based on their representation vectors. Finally, the system generates potential similar scholars for users in ASNs for the final recommendation. We evaluate the effectiveness of our model on five real-world datasets: SCHOLAT, Zhihu, APS, Yelp and Gowalla. The experimental results demonstrate that our model is effective and achieves promising improvements than the other competitive baselines.
format	article
author	Chengzhe Yuan Yi He Ronghua Lin Yong Tang
author_facet	Chengzhe Yuan Yi He Ronghua Lin Yong Tang
author_sort	Chengzhe Yuan
title	Graph Embedding for Scholar Recommendation in Academic Social Networks
title_short	Graph Embedding for Scholar Recommendation in Academic Social Networks
title_full	Graph Embedding for Scholar Recommendation in Academic Social Networks
title_fullStr	Graph Embedding for Scholar Recommendation in Academic Social Networks
title_full_unstemmed	Graph Embedding for Scholar Recommendation in Academic Social Networks
title_sort	graph embedding for scholar recommendation in academic social networks
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/bea4b9189c544d80b9be161905e413c9
work_keys_str_mv	AT chengzheyuan graphembeddingforscholarrecommendationinacademicsocialnetworks AT yihe graphembeddingforscholarrecommendationinacademicsocialnetworks AT ronghualin graphembeddingforscholarrecommendationinacademicsocialnetworks AT yongtang graphembeddingforscholarrecommendationinacademicsocialnetworks
_version_	1718405255179272192

Graph Embedding for Scholar Recommendation in Academic Social Networks

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