IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v563y2021ics0378437120307731.html
   My bibliography  Save this article

SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks

Author

Listed:
  • Agrawal, Smita
  • Patel, Atul

Abstract

Many real-world social networks such as brain graph, protein structure, food web, transportation system, World Wide Web, online social networks exist in the form of a complex network. In such complex networks, pattern identification or community detection requires extra effort in which identifying community is a significant problem in various research areas. Most of the clustering methods on graphs predominantly emphasize on the topological structure without considering connectivity between vertices and not bearing in mind the vertex properties/attributes or similarity-based on indirectly connected vertices. A novel clustering algorithm SAG-Cluster with K-medoids framework presented for detecting communities using a collaborative similarity measure which considers attribute importance in case the pair of disconnected nodes. A novel path strategy using classic Basel problem for the indirectly connected node as well as balanced attribute similarity and distance function is proposed. On two real data sets, experimental results show the effectiveness of SAG-Cluster with the comparison of other relevant methods.

Suggested Citation

  • Agrawal, Smita & Patel, Atul, 2021. "SAG Cluster: An unsupervised graph clustering based on collaborative similarity for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    • Handle: RePEc:eee:phsmap:v:563:y:2021:i:c:s0378437120307731
    DOI: 10.1016/j.physa.2020.125459
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437120307731
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2020.125459?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. Yan, Chao & Chang, Zhenhai, 2020. "Modularized convex nonnegative matrix factorization for community detection in signed and unsigned networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    2. Gu, Ke & Fan, Ying & Di, Zengru, 2020. "How to predict recommendation lists that users do not like," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    3. Zhou, HongFang & Li, Jin & Li, JunHuai & Zhang, FaCun & Cui, YingAn, 2017. "A graph clustering method for community detection in complex networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 551-562.
    4. Xu, Ying, 2020. "A spectral method to detect community structure based on the communicability modularity," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 537(C).
    5. Angelou, Konstantinos & Maragakis, Michael & Kosmidis, Kosmas & Argyrakis, Panos, 2020. "A hybrid model for the patent citation network structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 541(C).
    6. Shang, Ronghua & Luo, Shuang & Li, Yangyang & Jiao, Licheng & Stolkin, Rustam, 2015. "Large-scale community detection based on node membership grade and sub-communities integration," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 428(C), pages 279-294.
    7. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    8. Kumar, Sanjay & Panda, B.S., 2020. "Identifying influential nodes in Social Networks: Neighborhood Coreness based voting approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    9. Wang, Yichuan & Kung, LeeAnn & Byrd, Terry Anthony, 2018. "Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 3-13.
    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. Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "Community Detection Based on Node Influence and Similarity of Nodes," Mathematics, MDPI, vol. 10(6), pages 1-15, March.

    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. Sukeda, Issey & Miyauchi, Atsushi & Takeda, Akiko, 2023. "A study on modularity density maximization: Column generation acceleration and computational complexity analysis," European Journal of Operational Research, Elsevier, vol. 309(2), pages 516-528.
    2. Shang, Ronghua & Liu, Huan & Jiao, Licheng, 2017. "Multi-objective clustering technique based on k-nodes update policy and similarity matrix for mining communities in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 486(C), pages 1-24.
    3. Saoud, Bilal & Moussaoui, Abdelouahab, 2018. "A new hierarchical method to find community structure in networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 418-426.
    4. Nedioui, Med Abdelhamid & Moussaoui, Abdelouahab & Saoud, Bilal & Babahenini, Mohamed Chaouki, 2020. "Detecting communities in social networks based on cliques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 551(C).
    5. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    6. Basile, Luigi Jesus & Carbonara, Nunzia & Pellegrino, Roberta & Panniello, Umberto, 2023. "Business intelligence in the healthcare industry: The utilization of a data-driven approach to support clinical decision making," Technovation, Elsevier, vol. 120(C).
    7. Shang, Ronghua & Zhang, Weitong & Jiao, Licheng & Stolkin, Rustam & Xue, Yu, 2017. "A community integration strategy based on an improved modularity density increment for large-scale networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 471-485.
    8. Miraç Fatih İLGÜN, 2020. "Industry 4.0 and Transformation in Public Finance: An Assessment by Government Expenditures," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(44).
    9. Nguyen Dang Tuan, Minh & Nguyen Thanh, Nhan & Le Tuan, Loc, 2019. "Applying a mindfulness-based reliability strategy to the Internet of Things in healthcare – A business model in the Vietnamese market," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 54-68.
    10. Yu, Wantao & Zhao, Gen & Liu, Qi & Song, Yongtao, 2021. "Role of big data analytics capability in developing integrated hospital supply chains and operational flexibility: An organizational information processing theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    11. Jiang, Syuan-Yi, 2022. "Transition and innovation ecosystem – investigating technologies, focal actors, and institution in eHealth innovations," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    12. Brewis, Claire & Dibb, Sally & Meadows, Maureen, 2023. "Leveraging big data for strategic marketing: A dynamic capabilities model for incumbent firms," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    13. Khaled Naser Yousef Magableh & Selvi Kannan & Aladeen Yousef Rashid Hmoud, 2024. "Innovation Business Model: Adoption of Blockchain Technology and Big Data Analytics," Sustainability, MDPI, vol. 16(14), pages 1-25, July.
    14. Hoffmann, Stefan & Lasarov, Wassili & Dwivedi, Yogesh K., 2024. "AI-empowered scale development: Testing the potential of ChatGPT," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    15. Shaik, Aqueeb Sohail & Nazrul, Asif & Alshibani, Safiya Mukhtar & Agarwal, Vaishali & Papa, Armando, 2024. "Environmental and economical sustainability and stakeholder satisfaction in SMEs. Critical technological success factors of big data analytics," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    16. Rubbio, Iacopo & Bruccoleri, Manfredi, 2023. "Unfolding the relationship between digital health and patient safety: The roles of absorptive capacity and healthcare resilience," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    17. Brem, Alexander & Viardot, Eric & Nylund, Petra A., 2021. "Implications of the coronavirus (COVID-19) outbreak for innovation: Which technologies will improve our lives?," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    18. Inam ul Haq & Sofia Anwar & Abdul Quddoos & Faraz Riaz, 2022. "Estimation of Digital Transformation in South Asian Economies: An Application of Broad-Spectrum Approach," Journal of Economic Impact, Science Impact Publishers, vol. 4(3), pages 152-160.
    19. Justin Longo & Alan Rodney Dobell, 2018. "The Limits of Policy Analytics: Early Examples and the Emerging Boundary of Possibilities," Politics and Governance, Cogitatio Press, vol. 6(4), pages 5-17.
    20. Basile, L.J. & Carbonara, N. & Panniello, U. & Pellegrino, R., 2024. "The role of big data analytics in improving the quality of healthcare services in the Italian context: The mediating role of risk management," Technovation, Elsevier, vol. 133(C).

    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:phsmap:v:563:y:2021:i:c:s0378437120307731. 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.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    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.