Ego-centric Information Diffusion Visual Analysis in Social Network


Popular social media platforms could rapidly propagate vital information over social networks among a significant number of people. In this work we present D-Map (Diffusion Map), a novel visualization method to support exploration and analysis of social behaviors during such information diffusion and propagation on typical social media through a map metaphor. In D-Map, users who participated in reposting (i.e., resending a message initially posted by others) one central user's posts (i.e., a series of original tweets) are collected and mapped to a hexagonal grid based on their behavior similarities and in chronological order of the repostings. With additional interaction and linking, D-Map is capable of providing visual portraits of the influential users and describing their social behaviors. A comprehensive visual analysis system is developed to support interactive exploration with D-Map. We evaluate our work with real world social media data and find interesting patterns among users. Key players, important information diffusion paths, and interactions among social communities can be identified.


Siming Chen, Shuai Chen, Zhenhuang Wang, Jie Liang, Xiaoru Yuan, Nan Cao, Yadong Wu.
D-Map: Visual Analysis of Ego-centric Information Diffusion Patterns in Social Media.
In Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST'16), pages 41-50, Baltimore, USA, Oct. 23-28, 2016


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