| Project/Area Number |
19F19704
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 外国 |
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| Review Section |
Basic Section 62030:Learning support system-related
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| Research Institution | Tokyo University of Technology |
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Principal Investigator |
大山 恭弘 東京工科大学, 工学部, 教授 (00233289)
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| Co-Investigator(Kenkyū-buntansha) |
WANG FENG 東京工科大学, 工学部, 外国人特別研究員
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| Project Period (FY) |
2019-04-25 – 2021-03-31
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| Project Status |
Completed (Fiscal Year 2020)
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| 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)
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| Keywords | Influence maximization / Social big data / User influence / Social network analysis / Evaluation of influence / Influence Maximization / Recommendation Algorithm / Social Big Data / User Influence Eval. / Network Analysis |
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| 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.
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| 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.
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| Research Progress Status |
令和2年度が最終年度であるため、記入しない。
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| 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。
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