Recently, social network research has advanced rapidly with the prevalence of the online social websites and instant messaging social communications systems. In addition to utilizing a desktop computer to write blogs, upload photos and chat with friends, an explosively increasing number of people are now used to share personal location information to friends on the fly with mobile devices, thanks to the development of broadband wireless networks and location sensing technologies. These social network systems are usually characterized by complex network structures and abundant contextual information. Moreover, by incorporating the spatial dimension, mobile and location-based social networks are now immersed in people’s everyday life with numerous innovative websites, such Facebook Places, Foursquare, Buddy Beacon, FindMe, Loopt, and Weibo to facilitate a more convenient life. In addition, mobile social networks can be exploited to foster many interesting applications and analysis, such as recommendations of locations and friends travel planning, location-based viral marketing, community discovery, and group mobility and behavior modeling, etc.
Researchers are increasingly interested in addressing a wide spectrum of challenges in mobile social networks to extract useful knowledge, including identifying common static topological structures and dynamic evolutions of social networks, and exploiting location-based and contextual information embedded with mobile social networks to create useful insights. The insights can provide important implications on community discovery, anomaly detection, trend prediction with the applications in many domains, such as recommendation systems, information retrieval, future prediction, and so on. In light of the above crucial need, sophisticated data mining and query processing techniques on both social and spatial dimensions are demanding for extracting representative information from mobile social network. In addition, the data generated from social networks and social media streams at any time in any place have outpaced the capability to process, analyze, and mining those datasets. It is thus imperative to develop scalable and efficient algorithm for processing and mining Big Data generated from mobile social networks. In contrast to other areas in data management and mining, social and human factors are also important and thereby encouraged to be properly included in multidisciplinary and interdisciplinary research of mobile social networks. The 1st IEEE International Workshop on Mobile Data Management, Mining, and Computing on Social Networks (MobiSocial 2013) will serve as a forum for researchers and technologists to discuss the state-of-the-art, present their contributions, and set future directions in data management and mining for mobile social networks.