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Research on Influence Maximization Algorithm and Recommendation Application based on Social Big Data

Research Project

Project/Area Number 19F19704
Research Category

Grant-in-Aid for JSPS Fellows

Allocation TypeSingle-year Grants
Section外国
Review Section Basic Section 62030:Learning support system-related
Research InstitutionTokyo University of Technology

Principal Investigator

大山 恭弘  東京工科大学, 工学部, 教授 (00233289)

Co-Investigator(Kenkyū-buntansha) WANG FENG  東京工科大学, 工学部, 外国人特別研究員
Project Period (FY) 2019-04-25 – 2021-03-31
Project Status Completed (Fiscal Year 2020)
Budget Amount *help
¥2,300,000 (Direct Cost: ¥2,300,000)
Fiscal Year 2020: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2019: ¥1,200,000 (Direct Cost: ¥1,200,000)
KeywordsInfluence maximization / Social big data / User influence / Social network analysis / Evaluation of influence / Influence Maximization / Recommendation Algorithm / Social Big Data / User Influence Eval. / Network Analysis
Outline of Research at the Start

We are going to build a novel influence maximization algorithm and a recommendation application algorithm based on social big data. First, we establish a preprocess method for user influence information in online social networks. Next, we propose a user influence evaluation approach based on the social big data. Then, we devise a novel influence maximization algorithm to find the balance point between effectiveness and efficiency. Finally, we propose a group recommendation algorithm for education resources based on the top-k influential nodes.

Outline of Annual Research Achievements

Individuals in online social networks are linked with complicated relationships that lead to the complex characters of social networks. Users’ influence plays an important role in the process of information diffusion. The influence denotes an important ability that changes the behavior and thoughts of other people. We have been focusing on deriving some new analysis methods to study user influence in social big data. We carried out the study in 2020 as follows:
First, we established a new model of influence spread using fluid dynamics, which reveals the time-evolving process for influence spread. The problem of maximizing positive influence was formulated and a greedy algorithm, Fluidspread, was devised to solve the problem.
Then, a model of trust-based competitive influence diffusion was established to simulate the spread of positive and negative influence. An efficient algorithm of trust-based competitive influence maximization was developed through a heuristic pruning method.
Finally, an end-to-end method was devised that uses dual-task network embeddings to improve learning influence parameters, which is called a multi-dimensional influence-to-vector method. It learns dual-task network embeddings to jointly predict influence probabilities and cascade sizes.

Research Progress Status

令和2年度が最終年度であるため、記入しない。

Strategy for Future Research Activity

令和2年度が最終年度であるため、記入しない。

Report

(2 results)
  • 2020 Annual Research Report
  • 2019 Annual Research Report
  • Research Products

    (6 results)

All 2021 2020 2019 Other

All Int'l Joint Research (2 results) Journal Article (1 results) (of which Int'l Joint Research: 1 results,  Peer Reviewed: 1 results) Presentation (3 results) (of which Int'l Joint Research: 3 results)

  • [Int'l Joint Research] China University of Geosciences(中国)

    • Related Report
      2020 Annual Research Report
  • [Int'l Joint Research] China University of of Geosciences(中国)

    • Related Report
      2019 Annual Research Report
  • [Journal Article] Maximizing positive influence in competitive social networks: A trust-based solution2021

    • Author(s)
      Feng Wang, Jinhua She, Yasuhiro Ohyama, Wenjun Jiang, Geyong Min, Guojun Wang, Min Wu
    • Journal Title

      Information Sciences

      Volume: 546 Pages: 559-572

    • DOI

      10.1016/j.ins.2020.09.002

    • Related Report
      2020 Annual Research Report
    • Peer Reviewed / Int'l Joint Research
  • [Presentation] Fluidspread: A new method of maximizing positive influence in online social networks via fluid dynamics2020

    • Author(s)
      Feng Wang, Jinhua She, Yasuhiro Ohyama, and Min Wu
    • Organizer
      The 16th IEEE International Conference on Control and Automation (ICCA 2020)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Learning multiple network embeddings for social influence prediction2020

    • Author(s)
      Feng Wang, Jinhua She, Yasuhiro Ohyama, and Min Wu
    • Organizer
      The 21st World Congress of the International Federation of Automatic Control (21st IFAC World Congress)
    • Related Report
      2020 Annual Research Report
    • Int'l Joint Research
  • [Presentation] Deep-learning-based Identification of Influential Spreaders in Online Social Networks2019

    • Author(s)
      Feng Wang, Jinhua She, Yasuhiro Ohyama, and Min Wu
    • Organizer
      IEEE 45th Annual Conference of the Industrial Electronics Society (IECON 2019)
    • Related Report
      2019 Annual Research Report
    • Int'l Joint Research

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Published: 2019-05-29   Modified: 2024-03-26  

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