Advances in Social Network Analysis – Spatio-Temporal and Semantic Methods

Dear Colleagues,

Data from geospatial applications, such as social media, location-based service (LBS) and volunteered geographic information (VGI) platforms, has become a prominent source for modeling human behavior and for better understanding complex social dynamics in geographic spaces. The massive amount of multi-dimensional data (spatial, temporal, semantic) from these sources is typically unstructured and thus calls for an advance in data representation, modeling, analysis, and visualization for the successful transition from data to information. This Special Issue is inviting contributions that demonstrate integrated analysis of spatial, temporal, and semantic data from social networks, including their content, linkage, and structure, towards a better understanding of social behavior, human interaction patterns and the dynamic characteristics of real-world phenomena and events. This involves novel use of machine learning approaches, analysis frameworks, data mining, and (geo-)statistical methods to exploit unstructured content of social network data. This Special Issue also encourages the demonstration of new analytical tools, discussion of current data privacy and licensing issues, the exploration of data from lesser known social media, LBS, and VGI platforms, and the application of fusion methods of data across multiple platforms.

Prof. Dr. Hartwig H. Hochmair
Dr. Levente Juhász
Dr. Bernd Resch
Guest Editors

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