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Mining Behavioral Patterns from Mobile Big Data
PhD Thesis Proposal Defence Title: "Mining Behavioral Patterns from Mobile Big Data" by Mr. Tong LI Abstract: The widespread usage of Internet-connected mobile devices allows users' behavior to be recorded in large-scale and fine-grained digital traces in cyberspace. Such data, termed mobile big data, contain great social, economic, and academic values. Analyzing mobile big data has significant implications for all relevant stakeholders, ranging from smartphone manufacturers, network operators to app developers. In this thesis proposal, we propose three works towards an in-depth understanding and modeling users' behavior from mobile big data by leveraging large-scale and real-world datasets. Firstly, we investigate daily activity patterns across people's mobile app usage based on a real-world dataset consisting of 653,092 users with 971,818,946 mobile app usage records. We then discover five common patterns, including afternoon reading, nightly entertainment, pervasive socializing, commuting, and nightly socializing. We also show that people usually follow yesterday's activity patterns, but the patterns tend to deviate as the time-lapse increases. Secondly, we demonstrate that users' personality traits profoundly shape their digital behavior. We develop a multi-relational heterogeneous graph attention network (MRel-HGAN) to predict users' gender using spatiotemporal app usage behavior. By conducting extensive experiments on a real-world dataset covering 12,777 users with gender labels, MRel-HGAN achieves a precision of 73.71%, outperforming the best baseline by 4.4%. Lastly, we reveal the long-term evolution process of mobile app usage by conducting a longitudinal study on 1,465 users from 2012 to 2017. Our findings indicate that users' app usage indeed changes over time. However, the evolution processes in app-category usage and individual app usage are different in terms of popularity distribution, usage diversity, and correlations. In the end, we conclude this thesis proposal with future research directions and challenges related to mobile big data analysis. Date: Monday, 22 February 2021 Time: 4:00pm - 6:00pm Zoom Meeing: https://hkust.zoom.us/j/92011528543?pwd=bTBDVjJFblRCSCtDWWl4dm1uRjJIZz09 Committee Members: Dr. Pan Hui (Supervisor) Prof. Raymond Wong (Chairperson) Prof. James Kwok Dr. Dimitris Chatzopoulos **** ALL are Welcome ****