Abstract
A community structure is the most significant feature of social networks. Fusing the relation information and the attribute information to is necessary to detect community in the attributed social network. However, both relation and attribute information will have non-uniform quality because of the meaningless or erroneous noise in a social network. Moreover, the nodes that lose relation or attribute information will make the network into a partial network. In those cases, it is unrealistic to split users into different communities correctly without considering the noise and incompleteness in combination processing. To solve this problem, we propose a non-negative matrix factorization (NMF)-based community detection framework. In this framework, common and correct community structures can be identified effectively and disagreements can be reconciled by introducing two regularizations in combination processing. The experimental results confirm the superior performance of the method and demonstrate its effectiveness for a partial network.
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Acknowledgements
This work was supported by the National High Technology Research and Development Program of 2016YFB0100903 and the Beijing Municipal Science and Technology Commission Special Major (D171100005017002, D171100005117002) and the National Natural Science Foundation of China (U1664263).
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Han, W., Li, G., Zhang, X. (2018). Accurately Detecting Community with Large Attribute in Partial Networks. In: Geng, X., Kang, BH. (eds) PRICAI 2018: Trends in Artificial Intelligence. PRICAI 2018. Lecture Notes in Computer Science(), vol 11012. Springer, Cham. https://doi.org/10.1007/978-3-319-97304-3_49
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DOI: https://doi.org/10.1007/978-3-319-97304-3_49
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