Uncovering Research Topics of Academic Communities of Scientific Collaboration Network

In order to improve the quality of applications, such as recommendation or retrieval in knowledge-based service system, it is very helpful to uncover research topics of academic communities in scientific collaboration network (SCN). Previous research mainly focuses on network characteristics measure...

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Main Authors: Hongqi Han, Shuo Xu, Jie Gui, Xiaodong Qiao, Lijun Zhu, Han Zhang
Format: Article
Language:English
Published: Wiley 2014-04-01
Series:International Journal of Distributed Sensor Networks
Online Access:https://doi.org/10.1155/2014/529842
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author Hongqi Han
Shuo Xu
Jie Gui
Xiaodong Qiao
Lijun Zhu
Han Zhang
author_facet Hongqi Han
Shuo Xu
Jie Gui
Xiaodong Qiao
Lijun Zhu
Han Zhang
author_sort Hongqi Han
collection DOAJ
description In order to improve the quality of applications, such as recommendation or retrieval in knowledge-based service system, it is very helpful to uncover research topics of academic communities in scientific collaboration network (SCN). Previous research mainly focuses on network characteristics measurement and community evolution, but it remains largely understudied on how to uncover research topics of each community. This paper proposes a nonjoint approach, consisting of three simple steps: (1) to detect overlapping academic communities in SCN with the clique percolation method, (2) to discover underlying topics and research interests of each researcher with author-topic (AT) model, and (3) to label research topics of each community with top N most frequent collaborative topics between members belonging to the community. Extensive experimental results on NIPS (neural information processing systems) dataset show that our simple procedure is feasible and efficient.
format Article
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institution Kabale University
issn 1550-1477
language English
publishDate 2014-04-01
publisher Wiley
record_format Article
series International Journal of Distributed Sensor Networks
spelling doaj-art-a22a685f88184e6ea29e5a4acca4c7752025-02-03T06:43:15ZengWileyInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/529842529842Uncovering Research Topics of Academic Communities of Scientific Collaboration NetworkHongqi HanShuo XuJie GuiXiaodong QiaoLijun ZhuHan ZhangIn order to improve the quality of applications, such as recommendation or retrieval in knowledge-based service system, it is very helpful to uncover research topics of academic communities in scientific collaboration network (SCN). Previous research mainly focuses on network characteristics measurement and community evolution, but it remains largely understudied on how to uncover research topics of each community. This paper proposes a nonjoint approach, consisting of three simple steps: (1) to detect overlapping academic communities in SCN with the clique percolation method, (2) to discover underlying topics and research interests of each researcher with author-topic (AT) model, and (3) to label research topics of each community with top N most frequent collaborative topics between members belonging to the community. Extensive experimental results on NIPS (neural information processing systems) dataset show that our simple procedure is feasible and efficient.https://doi.org/10.1155/2014/529842
spellingShingle Hongqi Han
Shuo Xu
Jie Gui
Xiaodong Qiao
Lijun Zhu
Han Zhang
Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
International Journal of Distributed Sensor Networks
title Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
title_full Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
title_fullStr Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
title_full_unstemmed Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
title_short Uncovering Research Topics of Academic Communities of Scientific Collaboration Network
title_sort uncovering research topics of academic communities of scientific collaboration network
url https://doi.org/10.1155/2014/529842
work_keys_str_mv AT hongqihan uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork
AT shuoxu uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork
AT jiegui uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork
AT xiaodongqiao uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork
AT lijunzhu uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork
AT hanzhang uncoveringresearchtopicsofacademiccommunitiesofscientificcollaborationnetwork