In these days, strategic decision making and immediate action are becoming a complex task for companies and policymakers, since the environment is subject to emerging changes that might include unknown factors. When facing these challenges, companies are exposed to opportunities for growth, but also to threats. Therefore, they seek to explore and analyze large amounts of data to detect emerging changes, or socalled weak signals, that can help maintaining their competitive advantages and shaping up their future operational environments. But due to the increasing volume of daily produced data, scalable and automated computer-aided systems are needed to explore and extract these weak signals. To overcome the automation and scalability challenges, and capture early signs of change in a big data environment, we propose a framework for weak signals detection relying on the network topology. It is implemented under the Cocktail project framework whose goal is to create a real-time observatory of trends, innovations and weak signals circulating in the discourses of the food and health sectors on Twitter. This method analyses quantitatively the network local structure using the graphlets (particular type of motifs) to find weak signals. It provides accordingly qualitative elements that contextualise the identified signals, which will allow business experts to interpret and evaluate their dynamics to determine which ones may have a relevant future. After testing this method on different types of networks (we present two of them in this paper), we proved that it is able to detect weak signals and provides a quantifiable signature that allows better decision making.
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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