IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v598y2022ics0378437122002552.html
   My bibliography  Save this article

Influence maximization in social networks using effective community detection

Author

Listed:
  • Kazemzadeh, Farzaneh
  • Safaei, Ali Asghar
  • Mirzarezaee, Mitra

Abstract

Influence maximization problem aims to find a set of nodes with the highest diffusion in social networks in order to maximize diffusion in the graph by this set. A set of these nodes can be used to diffuse news, viruses , marketing and etc. To solve this problem, some algorithms have been proposed to help identify a set of nodes. Due to the low accuracy and high run time in selecting the set of nodes by the proposed algorithms, further studies should be performed in this area. This paper intends to solve the Richclub problem in selecting nodes, reduce the search space to decrease computational overhead, and achieve increases the algorithm accuracy by selecting high-charisma nodes. The Charismatic Transmission in Influence Maximization (CTIM) algorithm reduces the computational overhead by using community structure and pruning criteria. In this algorithm, seed nodes are selected using nodes that have high charismatic power. Also, these nodes have a high correlation with influential nodes in other communities, which causes optimal diffusion in social networks. As a result a set of seed nodes are selected by calculating the influence spread, which increases the algorithm accuracy. In all size ranges of seed sets and different data sets, experiment results show The CTIM algorithm performs well in influence spread. Also in The douban dataset significantly outperforms the PHG algorithm to as much as a 6.56% increase in influence spread and the execution time has decreased by 99%.

Suggested Citation

  • Kazemzadeh, Farzaneh & Safaei, Ali Asghar & Mirzarezaee, Mitra, 2022. "Influence maximization in social networks using effective community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 598(C).
    • Handle: RePEc:eee:phsmap:v:598:y:2022:i:c:s0378437122002552
    DOI: 10.1016/j.physa.2022.127314
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122002552
    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.2022.127314?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. Kolumbus, Yoav & Solomon, Sorin, 2021. "On the influence maximization problem and the percolation phase transition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 573(C).
    2. Aghaalizadeh, Saeid & Afshord, Saeid Taghavi & Bouyer, Asgarali & Anari, Babak, 2021. "A three-stage algorithm for local community detection based on the high node importance ranking in social networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 563(C).
    3. 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).
    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. Wu, Rui-Jie & Kong, Yi-Xiu & Di, Zengru & Zhang, Yi-Cheng & Shi, Gui-Yuan, 2022. "Analytical solution to the k-core pruning process," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).

    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, Yifan & Ng, S. Thomas, 2021. "Unveiling the rich-club phenomenon in urban mobility networks through the spatiotemporal characteristics of passenger flow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 584(C).
    2. Chen, Chunchun & Zhu, Wenjie & Peng, Bo, 2022. "Differentiated graph regularized non-negative matrix factorization for semi-supervised community detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 604(C).
    3. Liu, Qian & Wang, Jian & Zhao, Zhidan & Zhao, Na, 2022. "Relatively important nodes mining algorithm based on community detection and biased random walk with restart," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    4. Wang, Benyu & Gu, Yijun & Zheng, Diwen, 2022. "Community detection in error-prone environments based on particle cooperation and competition with distance dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    5. Han, Jihui & Zhang, Ge & Dong, Gaogao & Zhao, Longfeng & Shi, Yuefeng & Zou, Yijiang, 2024. "Exact analysis of generalized degree-based percolation without memory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 642(C).
    6. Jabari Lotf, Jalil & Abdollahi Azgomi, Mohammad & Ebrahimi Dishabi, Mohammad Reza, 2022. "An improved influence maximization method for social networks based on genetic algorithm," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 586(C).
    7. Shang, Ronghua & Zhang, Weitong & Zhang, Jingwen & Feng, Jie & Jiao, Licheng, 2022. "Local community detection based on higher-order structure and edge information," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 587(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:598:y:2022:i:c:s0378437122002552. 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.