Abstract
An incremental learning method for nodes’ classification is presented in the paper. In particular, there is proposed an active scheme algorithm for multi-class classification of nodes’ states that varies over time and depends on information spread in the network. Demonstration of the method is conducted using social network dataset. According to sent messages between nodes, the emotional state of the message sender updates each receiving node’s feature vector and the method tries to classify next emotional state of the receiver. The novelty of the proposed approach lies in applying incremental learning method for non-stationary network environment. There are demonstrated some properties of the proposed method in experiments with real data set, showing that the method can effectively classify the future state of nodes.
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Acknowledgements
The work was partly supported by The Polish National Science Centre, project no. 2013/09/B/ST6/02317 and 2016/21/D/ST6/02948 as well as the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 691152, RENOIR project; the Polish Ministry of Science and Higher Education fund for supporting internationally co-financed projects in 2016–2019 (agreement no. 3628/H2020/2016/2). The calculations were carried out in the Wroclaw Centre for Networking and SupercomputingFootnote 1, grant No 177.
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Kajdanowicz, T., Tagowski, K., Falkiewicz, M., Kazienko, P. (2018). Incremental Learning in Dynamic Networks for Node Classification. In: Alhajj, R., Hoppe, H., Hecking, T., Bródka, P., Kazienko, P. (eds) Network Intelligence Meets User Centered Social Media Networks. ENIC 2017. Lecture Notes in Social Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-90312-5_9
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