Research on Location-Based Social Networks (LBSNs) at the Mobile Systems and Networking Group at Fudan University

Background

We are interested in understanding the user behavior under the context of mobile social apps. We explore several aspects, including social graphs, user mobility patterns and malicious account detection.

Publications

• Qinge Xie, Qingyuan Gong, Xinlei He, Yang Chen*, Xin Wang, Haitao Zheng, Ben Y. Zhao. Trimming Mobile Applications for Bandwidth-Challenged Networks in Developing Regions. To appear: IEEE Transactions on Mobile Computing.

• Wanda Li, Zhiwei Xu, Yi Sun, Qingyuan Gong, Yang Chen*, Aaron Yi Ding, Xin Wang, Pan Hui. DeepPick: A Deep Learning Approach to Unveil Outstanding Users Ranking with Public Attainable Features. To appear: IEEE Transactions on Knowledge and Data Engineering (TKDE).

• Xinlei He, Qingyuan Gong, Yang Chen*, Yang Zhang, Xin Wang, Xiaoming Fu. DatingSec: Detecting Malicious Accounts in Dating Apps Using a Content-Based Attention Network. IEEE Transactions on Dependable and Secure Computing (TDSC), 2021, 18(5):2193-2208.

• Yang Chen, Jiyao Hu, Yu Xiao, Xiang Li, Pan Hui. Understanding the User Behavior of Foursquare: a Data-Driven Study on a Global Scale. IEEE Transactions on Computational Social Systems (TCSS), 2020, 7(4):1019-1032.

• Qingyuan Gong, Yang Chen, Jiyao Hu, Qiang Cao, Pan Hui, Xin Wang. Understanding Cross-site Linking in Online Social Networks. ACM Transactions on the Web (TWEB), 12(4):25:1-25:29.

• Rong Xie, Yang Chen, Qinge Xie, Yu Xiao, Xin Wang. We Know Your Preferences in New Cities: Mining and Modeling the Behavior of Travelers. IEEE Communications Magazine, 2018, 56(11):28-35.

• Qingyuan Gong, Yang Chen, Xinlei He, Zhou Zhuang, Tianyi Wang, Hong Huang, Xin Wang, Xiaoming Fu. DeepScan: Exploiting Deep Learning for Malicious Account Detection in Location-Based Social Networks. IEEE Communications Magazine, 2018, 56(11):21-27.

• Rong Xie, Yang Chen, Shihan Lin, Tianyong Zhang, Yu Xiao, Xin Wang. Understanding Skout Users' Mobility Patterns on a Global Scale: A Data-Driven Study. World Wide Web Journal (WWWJ), 2019, 22(6):2655–2673.

• Qian Zhou, Yang Chen, Chuanhao Ma, Fei Li, Yu Xiao, Xin Wang, Xiaoming Fu. Measurement and Analysis of the Reviews in Airbnb. Proc. of IFIP Networking Conference (Networking'18), Zurich, Switzerland, May 2018.

• Hao Zhao, Qingyuan Gong, Yang Chen, Jingrong Chen, Yong Li, Xiaoming Fu. This Place is Swarming: Using a Mobile Social App to Study Human Traffic in Cities. Proc. of 5th International Workshop on Crowd Assisted Sensing, Pervasive Systems and Communications (CASPer'18), co-located with the 16th IEEE International Conference on Pervasive Computing and Communications (PerCom'18), Athens, Greece, Mar. 2018.

• Yang Chen, Jiyao Hu, Hao Zhao, Yu Xiao, Pan Hui. Measurement and Analysis of the Swarm Social Network with Tens of Millions of Nodes. IEEE Access, 2018, 6:4547-4559.

• Shihan Lin, Rong Xie, Qinge Xie, Hao Zhao, Yang Chen. Understanding User Activity Patterns of the Swarm App: A Data-Driven Study. Proc. of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp'17), Poster Session, Hawaii, USA, Sep. 2017.

• Yue Huang, Yang Chen, Qian Zhou, Jin Zhao, Xin Wang. Where Are We Visiting? Measurement and Analysis of Venues in Dianping. Proc. of IEEE International Conference on Communications (ICC'16), Kuala Lumpur, Malaysia, May 2016.

• Yang Chen, Yuxi Yang, Jiyao Hu, Chenfan Zhuang. Measurement and Analysis of Tips in Foursquare. Proc. of the 14th IEEE International Conference on Pervasive Computing and Communications (PerCom'16), Work-in-Progress (WiP) session, Sydney, Australia, Mar. 2016.

Datasets

• An anonymized dataset of 60+ million Foursquare users with a focus on cross-site linking [Download]
• Review graph of the Airbnb network [Download]
• Social graph and number of check-ins of Foursquare's Swarm: all 60+ million users [Download]
• Check-In Data of Foursquare's Swarm: 1,562,452 check-ins generated by 5,112 Swarm users (collected in Apr.-May 2017) [Download]
• Foursquare Tip Data: Tips of 6.52 million Foursquare users (collected in Nov. 2015)