IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v514y2019icp796-818.html
   My bibliography  Save this article

C2IM: Community based context-aware influence maximization in social networks

Author

Listed:
  • Singh, Shashank Sheshar
  • Kumar, Ajay
  • Singh, Kuldeep
  • Biswas, Bhaskar

Abstract

Influence Maximization (IM) is an optimization problem in viral marketing to identify k most influential users in social networks. IM problem, with large-scale data, faces many challenges like time-efficiency, accuracy, and effectiveness of seed. To solve these challenges, we propose a Community based Context-aware Influence Maximization (C2IM) algorithm. C2IM uses a community-based framework to improve the time-efficiency that reduces the search space significantly. It considers user’s interests (known as topics) to address the effectiveness of seed. We extend the traditional information diffusion models (i.e., linear threshold and independent cascade) to Context-aware Linear Threshold model (CLT) and Context-aware Independent Cascade model (CIC) for influence spreading. We show that C2IM is NP-hard in nature under CLT and CIC models. To identify k most influential users, we first propose a Community Detection Algorithm (CDA) to partitions the network into sub-networks. We then devise a Non-Desirable nodes Finder (NDF) technique to identify non-desirable nodes. We introduce Seed Selection Algorithm (SSA) to compute most influential seed nodes based on diffusion degree of nodes. Experimental results show that the proposed algorithm performs better than CIM on influence spread and faster than TIM. Thus, C2IM algorithm is a trade-off between quality and efficiency.

Suggested Citation

  • Singh, Shashank Sheshar & Kumar, Ajay & Singh, Kuldeep & Biswas, Bhaskar, 2019. "C2IM: Community based context-aware influence maximization in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 514(C), pages 796-818.
    • Handle: RePEc:eee:phsmap:v:514:y:2019:i:c:p:796-818
    DOI: 10.1016/j.physa.2018.09.142
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437118312822
    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.2018.09.142?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.

    Citations

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


    Cited by:

    1. Kumar, Ajay & Singh, Shashank Sheshar & Singh, Kuldeep & Biswas, Bhaskar, 2020. "Link prediction techniques, applications, and performance: A survey," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    2. Mishra, Shivansh & Singh, Shashank Sheshar & Kumar, Ajay & Biswas, Bhaskar, 2022. "ELP: Link prediction in social networks based on ego network perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 605(C).
    3. Lin Zhang & Kan Li, 2021. "Influence Maximization Based on Backward Reasoning in Online Social Networks," Mathematics, MDPI, vol. 9(24), pages 1-17, December.
    4. Devi, Kalyanee & Tripathi, Rohit, 2023. "ASN: A method of optimality for seed identification in the influence diffusion process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    5. Bouyer, Asgarali & Beni, Hamid Ahmadi, 2022. "Influence maximization problem by leveraging the local traveling and node labeling method for discovering most influential nodes in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 592(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:514:y:2019:i:c:p:796-818. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.