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The rise of the Big Data paradigm has made it more feasible to track how personal networks evolve on social media, where auto-generated contact records and finegrained temporal data sequences help capture how and when interpersonal ties and contacts change their roles. Using a sample of matched survey data and social media records, we investigated the mechanisms by which indirect contacts ("degree-2 alters") transform into direct contacts ("degree-1 alters") from a Facebook user's (ego's) point of view. To highlight the temporal sequences, we assigned different roles to the same alters depending on how each of them is connected with ego at different periods of time. Multilevel event history analyses pinpoint several online actions and network features of ego, degree-1 alters, and degree-2 alters, as the key factors that contribute to the transformation from indirect contacts into direct contacts. RÉSUMÉL"essor du paradigme du Big Data a rendu plus réalisable le suivi de l"évolution des réseaux personnels sur les médias sociaux, où les enregistrements de contacts

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The data of egocentric networks (ego networks) are very important for evaluating and validating the algorithms and machine learning approaches in Online Social Networks (OSNs). Nevertheless, obtaining the ego network data from OSNs is not a trivial task. Conventional manual approaches are time-consuming, and only a small number of users would agree to contribute their data. This is because there are two important factors that should be considered simultaneously for this data acquisition task: i) users' willingness to contribute their data, and ii) the structure of the ego network. However, addressing the above two factors to obtain the more complete ego network data has not received much research attention. Therefore, in this paper, we make our first attempt to address this issue by proposing a new research problem, named Willingness Maximization for Ego Network Extraction in Online Social Networks (WMEgo), to identify a set of ego networks from the OSN such that the willingness of the users to contribute their data is maximized. We prove that WMEgo is NP-hard and propose a 1 2 (1 − 1)approximation algorithm, named Ego Network Identification with Maximum Willingness (EIMW). We conduct an evaluation study with 672 volunteers to validate the proposed WMEgo and EIMW, and perform extensive experiments on multiple real datasets to demonstrate the effectiveness and efficiency of our approach.