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A mathematical model for friend discovery from dynamic social graphs

Nowadays, social networking is popular. As such, numerous social networking sites (e.g., Facebook, YouTube, Instagram) are generating very large volumes of social data rapidly. Valuable knowledge and information is embedded into these big social data, and is awaiting to be analyzed and mined via social network analysis and mining. In general, social networks can be represented as graphs. Because of the dynamic nature of social networking, edges and/or vertices keep adding to (or deleting from) the graphs. We present in this paper a mathematical model for friend discovery from dynamic social graphs. In particular, we focus on both linear algebra and graph theory approaches to discover interesting social entities---such as active followers---from dynamic social networks represented as dynamic directional social graphs.