Detection of Event Precursors in Social Networks: A Graphlet-Based Method
Résumé
The increasing availability of data from online social networks attracts researchers' interest, who seek to build algorithms and machine learning models to analyze users' interactions and behaviors. Different methods have been developed to detect remarkable precursors preceding events, using text mining and Machine Learning techniques on documents, or using network topology with graph patterns. Our approach aims at analyzing social networks data, through a graphlets enumeration algorithm, to identify event precursors and to study their contribution to the event. We test the proposed method on two different types of social network data sets: real-world events (Lubrizol fire, EU law discussion), and general events (Facebook and MathOverflow). We also contextualize the results by studying the position (orbit) of important nodes in the graphlets, which are assumed as event precursors. After analysis of the results, we show that some graphlets can be considered precursors of events.
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