| 研究課題/領域番号 |
19F19704
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| 研究種目 |
特別研究員奨励費
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| 配分区分 | 補助金 |
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| 応募区分 | 外国 |
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| 審査区分 |
小区分62030:学習支援システム関連
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| 研究機関 | 東京工科大学 |
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研究代表者 |
大山 恭弘 東京工科大学, 工学部, 教授 (00233289)
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| 研究分担者 |
WANG FENG 東京工科大学, 工学部, 外国人特別研究員
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| 研究期間 (年度) |
2019-04-25 – 2021-03-31
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| 研究課題ステータス |
完了 (2020年度)
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| 配分額 *注記 |
2,300千円 (直接経費: 2,300千円)
2020年度: 1,100千円 (直接経費: 1,100千円)
2019年度: 1,200千円 (直接経費: 1,200千円)
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| キーワード | 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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| 研究開始時の研究の概要 |
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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| 研究実績の概要 |
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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| 現在までの達成度 (段落) |
令和2年度が最終年度であるため、記入しない。
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| 今後の研究の推進方策 |
令和2年度が最終年度であるため、記入しない。
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