| 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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