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Recent field studies revealed the merging trend of multiple key populations, making HIV intervention difficult because of the existence of multiple transmission modes in such complex multiplex social networks. In this research, we aim to address this challenge by developing a multiplex social network framework to capture the multi-mode transmission across two key populations. Based on the multiplex social network framework, we propose a new random search method, named Partition-based Random Search with Network and Memory Prioritization (PRS-NMP), to identify the optimal subset of high-value individuals in the social network for interventions. The performance of PRS-NMP based interventions is consistently better than benchmark nested partitions method and network-based metrics.

讲座内容：

Dr. Qingpeng Zhang is an assistant professor of CityU of Hong Kong. He received the B.S. degree in Automation from Huazhong University of Science and Technology in 2009, and the Ph.D. degree in Systems and Industrial Engineering with a minor in Management Information Systems from The University of Arizona in 2012. Prior to joining CityU, he worked as a Postdoctoral Research Associate with the Department of Computer Science at Rensselaer Polytechnic Institute. He also worked as an intern scientist at the Pacific Northwest National Laboratory and the Chinese Academy of Sciences. He is an Associate Editor for IEEE TITS and IEEE TCSS. His research interests include social informatics and social computing, complex networks, healthcare data analytics, and semantic web.

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