Automatic identification of informal social groups and places for geo-social recommendations
International Journal of Mobile Network Design and Innovation
Learning transportation mode from raw gps data for geographic applications on the web
Proceedings of the 17th international conference on World Wide Web
Micro-Blog: sharing and querying content through mobile phones and social participation
Proceedings of the 6th international conference on Mobile systems, applications, and services
AAMPL: accelerometer augmented mobile phone localization
Proceedings of the first ACM international workshop on Mobile entity localization and tracking in GPS-less environments
Mining interesting locations and travel sequences from GPS trajectories
Proceedings of the 18th international conference on World wide web
MobiClique: middleware for mobile social networking
Proceedings of the 2nd ACM workshop on Online social networks
WiMo: location-based emotion tagging
Proceedings of the 8th International Conference on Mobile and Ubiquitous Multimedia
WiFace: a secure geosocial networking system using WiFi-based multi-hop MANET
Proceedings of the 1st ACM Workshop on Mobile Cloud Computing & Services: Social Networks and Beyond
Distance matters: geo-social metrics for online social networks
WOSN'10 Proceedings of the 3rd conference on Online social networks
Least squares quantization in PCM
IEEE Transactions on Information Theory
Hi-index | 0.00 |
Mobile Social Network Services (MSNS) have collected massive amount of users' daily positioning information, which could be used for data mining to learn people's habits and behaviors. This paper proposes a novel clustering method to group nodes according to timestamp information, by analyzing the Time Heat Map (THM), i.e. the activity level distribution of a node during different time intervals. We have employed large amounts of anonymized positioning records coming from a real MSNS, which has extinguished this paper from other researches that use volunteers' daily GPS data. Experiment results have shown that this method not only reveals some interesting features of human activities in real world, but also can reflect clusters' geographical "interest fingerprints" affectively.