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- W2883402510 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." @default.
- W2883402510 created "2018-08-03" @default.
- W2883402510 creator A5023212539 @default.
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- W2883402510 date "2018-01-01" @default.
- W2883402510 modified "2023-09-27" @default.
- W2883402510 title "Accurately Detecting Community with Large Attribute in Partial Networks" @default.
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- W2883402510 doi "https://doi.org/10.1007/978-3-319-97304-3_49" @default.
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