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Community detection: Topological vs. topical

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  • Ding, Ying

Abstract

The evolution of the Web has promoted a growing interest in social network analysis, such as community detection. Among many different community detection approaches, there are two kinds that we want to address: one considers the graph structure of the network (topology-based community detection approach); the other one takes the textual information of the network nodes into consideration (topic-based community detection approach). This paper conducted systematic analysis of applying a topology-based community detection approach and a topic-based community detection approach to the coauthorship networks of the information retrieval area and found that: (1) communities detected by the topology-based community detection approach tend to contain different topics within each community; and (2) communities detected by the topic-based community detection approach tend to contain topologically-diverse sub-communities within each community. The future community detection approaches should not only emphasize the relationship between communities and topics, but also consider the dynamic changes of communities and topics.

Suggested Citation

  • Ding, Ying, 2011. "Community detection: Topological vs. topical," Journal of Informetrics, Elsevier, vol. 5(4), pages 498-514.
    • Handle: RePEc:eee:infome:v:5:y:2011:i:4:p:498-514
    DOI: 10.1016/j.joi.2011.02.006
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    5. Tian, Yunpei & Li, Gang & Mao, Jin, 2023. "Predicting the evolution of scientific communities by interpretable machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
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    7. Curci, Ylenia & Mongeau Ospina, Christian A., 2016. "Investigating biofuels through network analysis," Energy Policy, Elsevier, vol. 97(C), pages 60-72.
    8. Li, Daifeng & Ding, Ying & Shuai, Xin & Bollen, Johan & Tang, Jie & Chen, Shanshan & Zhu, Jiayi & Rocha, Guilherme, 2012. "Adding community and dynamic to topic models," Journal of Informetrics, Elsevier, vol. 6(2), pages 237-253.
    9. Lu Huang & Xiang Chen & Yi Zhang & Changtian Wang & Xiaoli Cao & Jiarun Liu, 2022. "Identification of topic evolution: network analytics with piecewise linear representation and word embedding," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5353-5383, September.
    10. Beibei Hu & Xianlei Dong & Chenwei Zhang & Timothy D. Bowman & Ying Ding & Staša Milojević & Chaoqun Ni & Erjia Yan & Vincent Larivière, 2015. "A lead-lag analysis of the topic evolution patterns for preprints and publications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2643-2656, December.
    11. Xie, Qing & Zhang, Xinyuan & Ding, Ying & Song, Min, 2020. "Monolingual and multilingual topic analysis using LDA and BERT embeddings," Journal of Informetrics, Elsevier, vol. 14(3).
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    13. Viana, Matheus P. & Amancio, Diego R. & da F. Costa, Luciano, 2013. "On time-varying collaboration networks," Journal of Informetrics, Elsevier, vol. 7(2), pages 371-378.
    14. Yan, Erjia & Ding, Ying & Milojević, Staša & Sugimoto, Cassidy R., 2012. "Topics in dynamic research communities: An exploratory study for the field of information retrieval," Journal of Informetrics, Elsevier, vol. 6(1), pages 140-153.
    15. Xiaoguang Wang & Qikai Cheng & Wei Lu, 2014. "Analyzing evolution of research topics with NEViewer: a new method based on dynamic co-word networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1253-1271, November.
    16. Theresa Velden & Shiyan Yan & Carl Lagoze, 2017. "Mapping the cognitive structure of astrophysics by infomap clustering of the citation network and topic affinity analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 1033-1051, May.
    17. Righi, Riccardo & Samoili, Sofia & López Cobo, Montserrat & Vázquez-Prada Baillet, Miguel & Cardona, Melisande & De Prato, Giuditta, 2020. "The AI techno-economic complex System: Worldwide landscape, thematic subdomains and technological collaborations," Telecommunications Policy, Elsevier, vol. 44(6).
    18. Chen, Guo & Xiao, Lu, 2016. "Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods," Journal of Informetrics, Elsevier, vol. 10(1), pages 212-223.
    19. Erjia Yan & Ying Ding & Elin K. Jacob, 2012. "Overlaying communities and topics: an analysis on publication networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(2), pages 499-513, February.
    20. He, Bing & Ding, Ying & Tang, Jie & Reguramalingam, Vignesh & Bollen, Johan, 2013. "Mining diversity subgraph in multidisciplinary scientific collaboration networks: A meso perspective," Journal of Informetrics, Elsevier, vol. 7(1), pages 117-128.
    21. Qian-Jin Zong & Hong-Zhou Shen & Qin-Jian Yuan & Xiao-Wei Hu & Zhi-Ping Hou & Shun-Guo Deng, 2013. "Doctoral dissertations of Library and Information Science in China: A co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(2), pages 781-799, February.
    22. Hakyeon Lee & Pilsung Kang, 2018. "Identifying core topics in technology and innovation management studies: a topic model approach," The Journal of Technology Transfer, Springer, vol. 43(5), pages 1291-1317, October.

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