Research community detection from multi-relation researcher network based on structure/attribute similarities
LIU Ping; CHEN Fenglin; MA Yunlu; HU Yuehong; FANG Kai; MENG Rui; Liu Ping (E-mail:p.liu@whu.edu.cn)
2013-03-25
Source PublicationChinese Journal of Library and Information Science
ISSN1674-3393
Volume6Issue:1Pages:14-32
Abstract

Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.

Design/methodology/approach: A heterogeneous researcher network has been constructed by combining multiple relations of academic researchers. Vertex attributes and their similarities were considered and calculated. An approach has been proposed and tested to detect research community in research organizations based on this multi-relation researcher network.

Findings: Detection of topologically well-connected, semantically coherent and meaningful research community was achieved.

Research limitations: The sample size of evaluation experiments was relatively small. In the present study, a limited number of 72 researchers were analyzed for constructing researcher network and detecting research community. Therefore, a large sample size is required to give more information and reliable results.

Practical implications: The proposed multi-relation researcher network and approaches for discovering research communities of similar research interests will contribute to collective innovation behavior such as brainstorming and to promote interdisciplinary cooperation.

Originality/value: Recent researches on community detection devote most efforts to singlerelation researcher networks and put the main focus on the topological structure of networks. In reality, there exist multi-relation social networks. Vertex attribute also plays an important role in community detection. The present study combined multiple single-relational researcher networks into a multi-relational network and proposed a structure-attribute clustering method for detecting research community in research organizations.

;

Purpose: This paper aims to provide a method to detect research communities based on research interest in researcher network, which combines the topological structure and vertex attributes in a unified manner.

Design/methodology/approach: A heterogeneous researcher network has been constructed by combining multiple relations of academic researchers. Vertex attributes and their similarities were considered and calculated. An approach has been proposed and tested to detect research community in research organizations based on this multi-relation researcher network.

Findings: Detection of topologically well-connected, semantically coherent and meaningful research community was achieved.

Research limitations: The sample size of evaluation experiments was relatively small. In the present study, a limited number of 72 researchers were analyzed for constructing researcher network and detecting research community. Therefore, a large sample size is required to give more information and reliable results.

Practical implications: The proposed multi-relation researcher network and approaches for discovering research communities of similar research interests will contribute to collective innovation behavior such as brainstorming and to promote interdisciplinary cooperation.

Originality/value: Recent researches on community detection devote most efforts to singlerelation researcher networks and put the main focus on the topological structure of networks. In reality, there exist multi-relation social networks. Vertex attribute also plays an important role in community detection. The present study combined multiple single-relational researcher networks into a multi-relational network and proposed a structure-attribute clustering method for detecting research community in research organizations.

KeywordCommunity Detection Multi-relation Social Network Semantic Association
Subject Area编辑出版
URL查看原文
Funding OrganizationThis work is supported by the National Natural Science Foundation of China (Grant No.: 71203164).
Document Type期刊论文
Identifierhttp://ir.las.ac.cn/handle/12502/6148
CollectionJournal of Data and Information Science_Chinese Journal of Library and Information Science-2013
Corresponding AuthorLiu Ping (E-mail:p.liu@whu.edu.cn)
Recommended Citation
GB/T 7714
LIU Ping,CHEN Fenglin,MA Yunlu,et al. Research community detection from multi-relation researcher network based on structure/attribute similarities[J]. Chinese Journal of Library and Information Science,2013,6(1):14-32.
APA LIU Ping.,CHEN Fenglin.,MA Yunlu.,HU Yuehong.,FANG Kai.,...&Liu Ping .(2013).Research community detection from multi-relation researcher network based on structure/attribute similarities.Chinese Journal of Library and Information Science,6(1),14-32.
MLA LIU Ping,et al."Research community detection from multi-relation researcher network based on structure/attribute similarities".Chinese Journal of Library and Information Science 6.1(2013):14-32.
Files in This Item: Download All
File Name/Size DocType Version Access License
Liu Ping.pdf(8639KB) 开放获取LicenseView Download
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[LIU Ping]'s Articles
[CHEN Fenglin]'s Articles
[MA Yunlu]'s Articles
Baidu academic
Similar articles in Baidu academic
[LIU Ping]'s Articles
[CHEN Fenglin]'s Articles
[MA Yunlu]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[LIU Ping]'s Articles
[CHEN Fenglin]'s Articles
[MA Yunlu]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Liu Ping.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.