3 years ago

Analysis of group evolution prediction in complex networks.

Piotr Bródka, Przemysław Kazienko, Michał Koziarski, Stanisław Saganowski

In a world, in which the acceptance and the social community membership is highly desired, the ability to predict social group evolution appears to be a fascinating research task, yet very complex. Therefore, the problem decomposition has been performed, and a new, adaptable and generic method for group evolution prediction in social networks (called GEP) is proposed in this paper. The work also contains extensive evaluation of the GEP method for many real-world social networks, including (1) analysis of numerous parameters (time window type and size, community detection method, evolution chain length, classifier used, etc.), (2) comparative analysis against other existing methods, (3) adaptation of the transfer learning concept to group evolution prediction, (4) enhancing the classification model with a more appropriate training set, and (5) prediction of more distant and multiple following future events. Additionally, many new predictive features reflecting the community state at a given time are proposed as well as rankings of the features most valuable for the classification process are provided. Moreover, the work identified a number of problems of existing methods for evolution prediction. The most severe are methodological issues, a narrow application area, insufficient validation, superficial descriptions of the methods and conducted experiments, as well as lack or unreliable comparisons with other methods.

Publisher URL: http://arxiv.org/abs/1711.01867

DOI: arXiv:1711.01867v1

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