IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v15y2021i3s1751157721000444.html
   My bibliography  Save this article

Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength

Author

Listed:
  • Wang, Ruby W.
  • Wei, Shelia X.
  • Ye, Fred Y.

Abstract

Based on the analytical methodology of homogeneous networks, we present a novel method to extract a core structure from a heterogeneous network. By extending two forms of meta-paths to represent the relationships between attribute edges, we propose the meta-path strength as a measure of the link strength of attribute edges in a heterogeneous information network. Inspired by the h-subnet method for weighted complex networks, we identify important attribute edges based on the h-cutoff of meta-path strengths. Additionally, important base edges can be filtered according to the base nodes on the retained attribute edges. Therefore, a heterogeneous h-subnet can be obtained by combining important attribute edges and base edges. Two bibliographic information networks are used to evaluate the proposed method empirically, and the results indicate that the extracted heterogeneous h-subnets contain less than 1% of the nodes and edges of the original networks and can cover different features of at least one of several other core structures.

Suggested Citation

  • Wang, Ruby W. & Wei, Shelia X. & Ye, Fred Y., 2021. "Extracting a core structure from heterogeneous information network using h-subnet and meta-path strength," Journal of Informetrics, Elsevier, vol. 15(3).
    • Handle: RePEc:eee:infome:v:15:y:2021:i:3:s1751157721000444
    DOI: 10.1016/j.joi.2021.101173
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157721000444
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.joi.2021.101173?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zhao, Star X. & Rousseau, Ronald & Ye, Fred Y., 2011. "h-Degree as a basic measure in weighted networks," Journal of Informetrics, Elsevier, vol. 5(4), pages 668-677.
    2. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
    3. Linyuan Lü & Tao Zhou & Qian-Ming Zhang & H. Eugene Stanley, 2016. "The H-index of a network node and its relation to degree and coreness," Nature Communications, Nature, vol. 7(1), pages 1-7, April.
    4. Nykl, Michal & Ježek, Karel & Fiala, Dalibor & Dostal, Martin, 2014. "PageRank variants in the evaluation of citation networks," Journal of Informetrics, Elsevier, vol. 8(3), pages 683-692.
    5. Ying Ding & Erjia Yan & Arthur Frazho & James Caverlee, 2009. "PageRank for ranking authors in co‐citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(11), pages 2229-2243, November.
    6. Anil, Akash & Singh, Sanasam Ranbir, 2020. "Effect of class imbalance in heterogeneous network embedding: An empirical study," Journal of Informetrics, Elsevier, vol. 14(2).
    7. Erjia Yan & Ying Ding & Cassidy R. Sugimoto, 2011. "P‐Rank: An indicator measuring prestige in heterogeneous scholarly networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 62(3), pages 467-477, March.
    8. Korn, A. & Schubert, A. & Telcs, A., 2009. "Lobby index in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(11), pages 2221-2226.
    9. Jensen, Scott & Liu, Xiaozhong & Yu, Yingying & Milojevic, Staša, 2016. "Generation of topic evolution trees from heterogeneous bibliographic networks," Journal of Informetrics, Elsevier, vol. 10(2), pages 606-621.
    10. Zhao, Star X. & Ye, Fred Y., 2012. "Exploring the directed h-degree in directed weighted networks," Journal of Informetrics, Elsevier, vol. 6(4), pages 619-630.
    11. Erjia Yan & Ying Ding & Cassidy R. Sugimoto, 2011. "P-Rank: An indicator measuring prestige in heterogeneous scholarly networks," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(3), pages 467-477, March.
    12. Steven H. Strogatz, 2001. "Exploring complex networks," Nature, Nature, vol. 410(6825), pages 268-276, March.
    13. Star X. Zhao & Paul L. Zhang & Jiang Li & Alice M. Tan & Fred Y. Ye, 2014. "Abstracting the core subnet of weighted networks based on link strengths," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(5), pages 984-994, May.
    14. Li, Jichao & Ge, Bingfeng & Yang, Kewei & Chen, Yingwu & Tan, Yuejin, 2017. "Meta-path based heterogeneous combat network link prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 482(C), pages 507-523.
    15. Xiaorui Jiang & Xiaoping Sun & Zhe Yang & Hai Zhuge & Jianmin Yao, 2016. "Exploiting heterogeneous scientific literature networks to combat ranking bias: Evidence from the computational linguistics area," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(7), pages 1679-1702, July.
    16. András Schubert & András Korn & András Telcs, 2009. "Hirsch-type indices for characterizing networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 78(2), pages 375-382, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wei, Shelia X. & Tong, Tong & Rousseau, Ronald & Wang, Wanru & Ye, Fred Y., 2022. "Relations among the h-, g-, ψ-, and p-index and offset-ability," Journal of Informetrics, Elsevier, vol. 16(4).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhang, Fang & Wu, Shengli, 2020. "Predicting future influence of papers, researchers, and venues in a dynamic academic network," Journal of Informetrics, Elsevier, vol. 14(2).
    2. Shunshun Shi & Wenyu Zhang & Shuai Zhang & Jie Chen, 2018. "Does prestige dimension influence the interdisciplinary performance of scientific entities in knowledge flow? Evidence from the e-government field," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 1237-1264, November.
    3. Rousseau, Ronald & Zhao, Star X., 2015. "A general conceptual framework for characterizing the ego in a network," Journal of Informetrics, Elsevier, vol. 9(1), pages 145-149.
    4. Zhang, Ronda J. & Ye, Fred Y., 2020. "Measuring similarity for clarifying layer difference in multiplex ad hoc duplex information networks," Journal of Informetrics, Elsevier, vol. 14(1).
    5. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2018. "Bi-directional h-index: A new measure of node centrality in weighted and directed networks," Journal of Informetrics, Elsevier, vol. 12(1), pages 299-314.
    6. Yan, Xiangbin & Zhai, Li & Fan, Weiguo, 2013. "C-index: A weighted network node centrality measure for collaboration competence," Journal of Informetrics, Elsevier, vol. 7(1), pages 223-239.
    7. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
    8. András Schubert, 2015. "Rescaling the h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1647-1653, February.
    9. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2022. "Analysing academic paper ranking algorithms using test data and benchmarks: an investigation," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(7), pages 4045-4074, July.
    10. Wei, Shelia X. & Tong, Tong & Rousseau, Ronald & Wang, Wanru & Ye, Fred Y., 2022. "Relations among the h-, g-, ψ-, and p-index and offset-ability," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Wen Zhou & Jiayi Gu & Yifan Jia, 2018. "h-Index-based link prediction methods in citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 381-390, October.
    12. Zhao, Star X. & Tan, Alice M. & Yu, Shuang & Xu, Xin, 2018. "Analyzing the research funding in physics: The perspective of production and collaboration at institution level," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 508(C), pages 662-674.
    13. Fang Zhang & Shengli Wu, 2021. "Measuring academic entities’ impact by content-based citation analysis in a heterogeneous academic network," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7197-7222, August.
    14. Zhao, Star X. & Ye, Fred Y., 2012. "Exploring the directed h-degree in directed weighted networks," Journal of Informetrics, Elsevier, vol. 6(4), pages 619-630.
    15. Yu, Dejian & Sheng, Libo, 2021. "Influence difference main path analysis: Evidence from DNA and blockchain domain citation networks," Journal of Informetrics, Elsevier, vol. 15(4).
    16. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.
    17. Zhai, Li & Yan, Xiangbin & Zhang, Guojing, 2013. "A centrality measure for communication ability in weighted network," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(23), pages 6107-6117.
    18. András Schubert, 2012. "A Hirsch-type index of co-author partnership ability," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(1), pages 303-308, April.
    19. R. G. Raj & A. N. Zainab, 2012. "Relative measure index: a metric to measure the quality of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 305-317, November.
    20. Jean J. Wang & Sarah X. Shao & Fred Y. Ye, 2021. "Identifying 'seed' papers in sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6001-6011, July.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:15:y:2021:i:3:s1751157721000444. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.