- General Chairs:
- Christian S. Jensen,
- Wang-Chien Lee,
- Program Chairs:
- Yu Zheng,
- Mohamed F. Mokbel
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.
Proceeding Downloads
User association analysis of locales on location based social networks
In recent years, location-based social networks (LBSNs) have received high attention. While this new breed of social networks is nascent, there is no large-scale analysis conducted to investigate the associations among users in locales of the network. ...
Sensing urban mobility with taxi flow
The analysis of taxi flow can help better understand the urban mobility. In this work, we analyze 177, 169 taxi trips collected in Lisbon, Portugal, to explore the relationships between pick-up and drop-off locations; the behavior between the previous ...
Spatial-social network visualization for exploratory data analysis
There has been considerable interest in applying social network analysis methods to geographically embedded networks such as population migration and international trade. However, research is hampered by a lack of support for exploratory spatial-social ...
Discovering personalized routes from trajectories
Most people usually drive their familiar routes to work and are concerned about the traffic on their way to work. If a driver's preferred route is known, the traffic congestion information on his/her way to work will be reported in time. However, the ...
Storing routes in socio-spatial networks and supporting social-based route recommendation
Cellular phones and GPS-based navigation systems allow recording the location history of users, to find places the users frequently visit and routes along which the users frequently travel. This provides associations between users and geographic ...
Towards an online detection of pedestrian flocks in urban canyons by smoothed spatio-temporal clustering of GPS trajectories
Detecting pedestrians moving together through public spaces can provide relevant information for many location-based social applications. In this work we present an online method to detect such pedestrian flocks by spatio-temporal clustering of location ...
Towards trajectory-based experience sharing in a city
As location-aware mobile devices such as smartphones have now become prevalent, people are able to easily record their trajectories in daily lives. Such personal trajectories are a very promising means to share their daily life experiences, since ...
Space-time dynamics of topics in streaming text
Human-generated textual data streams from services such as Twitter increasingly become geo-referenced. The spatial resolution of their coverage improves quickly, making them a promising instrument for sensing various aspects of evolution and dynamics of ...
Identification of live news events using Twitter
Twitter presents a source of information that cannot easily be obtained anywhere else. However, though many posts on Twitter reveal up-to-the-minute information about events in the world or interesting sentiments, far more posts are of no interest to ...
Crowd-based urban characterization: extracting crowd behavioral patterns in urban areas from Twitter
The advent of location-based social networking sites provides an open sharing space of crowd-sourced lifelogs that can be regarded as a novel source to monitor massive crowds' lifestyles in the real world. In this paper, we challenge to analyze urban ...
Extracting urban patterns from location-based social networks
Social networks attract lots of new users every day and absorb from them information about events and facts happening in the real world. The exploitation of this information can help identifying mobility patterns that occur in an urban environment as ...
Geo-social recommendations based on incremental tensor reduction and local path traversal
Social networks have evolved with the combination of geographical data, into Geo-social networks (GSNs). GSNs give users the opportunity, not only to communicate with each other, but also to share images, videos, locations, and activities. The latest ...
Tag recommendation for georeferenced photos
This paper presents methods for annotating georeferenced photos with descriptive tags, exploring the annotations for other georeferenced photos which are available at online repositories like Flickr. Specifically, by using the geospatial coordinates ...
Collaborative activity recognition via check-in history
With the growing number of smartphones and increasing interest of location-based social network, check-in becomes more and more popular. Check-in means a user has visited a location, e.g., a Point of Interest (POI). The category of the POI implies the ...
Recommendations
Acceptance Rates
| Year | Submitted | Accepted | Rate |
|---|---|---|---|
| LBSN '09 | 15 | 8 | 53% |
| Overall | 15 | 8 | 53% |