Social network and mining research has advanced rapidly with the prevalence of the online social websites and instant messaging social communications systems. In addition, thanks to the recent advances in deep learning, many novel applications with mobile devices and social networks have been proposed and deployed. 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 via numerous innovative websites. In addition, mobile social networks can be exploited to foster many interesting applications and analysis, such as recommendations of locations and travel planning of friends, location-based viral marketing, community discovery, group mobility and behavior modeling.

Researchers are increasingly interested in addressing a wide spectrum of challenges in mobile social networks to extract useful knowledge and exploiting location-based and contextual information embedded with mobile social networks to find out 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, machine learning, 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.