LBSN 2011 - 3rd ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN) 2011

Social networks have been prevalent on the Internet and become a hot research topic attracting many professionals from a variety of fields. By adding a location dimension, we can bring online social networks back to the physical world and share our real-life experiences in the virtual world conveniently. In location Based Social Networks (LBSN), people cannot only track and share location-related information with each other via either mobile devices or desktop computers, but also leverage collaborative social knowledge learned from user-generated and location-related contents. As location is one of the most important properties in people’s everyday lives, LBSN will bridge the gap between online societies and the physical world and enable a lot of novel applications changing the way we live, such as travel planning, location/friend recommendations, community discovery, human mobility modeling and user activity analysis. The technology derived from LBSN, e.g., location trajectory mining and retrieval, can also be applied to a multitude of other research areas including biology, sociology, geography, and climatology, etc.

The objective of this workshop is to provide professionals, researchers, and technologists with a single forum where they can discuss and share the state-of-the-art of LBSN development and applications, present their ideas and contributions, and set future directions in emerging innovative research for location based social networks.

Topics of Interest
Topics of interest include, but not limited to, the following aspects :

Spatial and spatio-temporal data mining in user-centric scenarios
Moving object tracking, indexing and retrieval for social applications
Trajectory compressing and simplification
Trajectory mining, pattern recognition, and knowledge discovery
Location privacy and security
Uncertainty of location and trajectory in modeling, inference, and querying
Activity recognition and sensing for social applications
Location identification from sensor data for social applications
User behavior modeling using physical sensor data

Semantic meaning and knowledge discovery from location-related data
User similarity computing based on location-related information
Social structure detection from location-related data
Location and friend recommendations
Hot spots, significant places, and interesting locations detection
Location-tagged media sharing and mining
Human-computer interaction in location-based social networks
Mobile and ubiquitous computing for location-based social networks
Information retrieval in location-based social networks