Trends detection in social networks is possible via a multitude of models with different characteristics. These models are pre-defined and rigid which creates the need to expose the social network graph to data scientists to introduce the human-element in trends detection. However, inspecting large social network graphs visually is tiresome. We tackle this problem by providing effective graph summarizations aimed at the application of geo-correlated trends detection in social networks.
['Colin Biafore', 'Faisal Nawab']
Graph Summarization for Geo-correlated Trends Detection in Social Networks