报告题目：Indirect influence in social networks as an induced percolation phenomenon
报告时间：10月25日（周二）上午08:30
报告地点：腾讯会议700-620-526、闵行校区物理楼314会议室
主讲人：胡延庆 副教授
主持人：唐明 教授
报告人简介：
Yanqing Hu received his PhD degree from Beijing Normal University in 2011. He was a Postdoctoral Researcher at the Levich Institute of City University of New York, from 2011 to 2013. Currently, he is an Associate Professor at Department of Statistics and Data Science of Southern University of Science and Technology. His research interests mainly focus on using big data to explore the mechanisms inside complex systems, such as the spreading of human behavior, the resilience of brain and infrastructure networks, the network structure predictability and formation process, etc. Here is his homepage: www.huyanqing.com
报告内容简介：
Percolation theory has been widely used to study phase transitions in network systems. It has also successfully explained various macroscopic spreading phenomena across different fields. Yet, the theoretical frameworks have been focusing on direct interactions among nodes, while recent empirical observations have shown that indirect interactions are common in many network systems like social and ecological networks, among others. By investigating the detailed mechanism of both direct and indirect influence on scientific collaboration networks, here we show that indirect influence can play the dominant role in behavioral influence. To address the lack of theoretical understanding of such indirect influence on the macroscopic behavior of the system, we propose a new percolation mechanism of indirect interactions called induced percolation. Surprisingly, our new model exhibits unique anisotropy property. Specifically, directed networks show first order abrupt transitions as opposed to the second order continuous transition in the same network structure but with undirected links. A mix of directed and undirected links leads to rich hybrid phase transitions. Furthermore, a unique feature of non-monotonic pattern is observed in network connectivities near the critical point. We also present an analytical framework to characterize the proposed induced percolation, paving way to further understand network dynamics with indirect interactions.
