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Discovering Users-Centric Hierarchical Process Models in Social Networking Services
Josue Obregon,Jae-Yoon Jung(정재윤) 대한산업공학회 2014 대한산업공학회 추계학술대회논문집 Vol.2014 No.11
Social network analysis has been an interest topic among researchers since the middle of the last century because it helps to understand social behaviors based on the interactions among people. Moreover, at the beginning of 2000’s, online social networking services such as Facebook and Twitter have emerged rapidly and have become very huge data sources for social media analysis. A few studies related to processes that occur on a social network have been published, focused on topics like information diffusion and more precisely modeling diffusion process. On the other hand, process mining is an emergent discipline that combines process modeling and analysis with data mining techniques and offers insights over business process data stored in the so-called event logs, recorded everyday by information systems. Those event logs are the starting point of one process mining type known as process discovery, in which a process model is discovered based only on the data extracted from an event log file. In this paper a novel approach is presented, in which online social network data is used for process mining to discover hierarchical process models. The data first is preprocessed by means of community detection techniques in order to reduce its complexity, and an extended Heuristics Miner is then applied to the discovered communities to give insights about information diffusion process on the network. An experiment with real world Facebook data is conducted and its results are evaluated and discussed.