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• W2907171162 abstract "From the beginning of 21st century, most people, especially the young ones, used to share whatever they want from their stuff like photo, chats, opinion, interests, accomplishments, and so on, over the social network day after day. One of the quite popular debates is about that if the social network sites preserve the individual privacy or not. The anonymizing techniques are famous techniques which provide privacy preservation for the published structural data. The proposed method aims to preserve the individuals’ privacy in the weighted social network network. This research proposes a (K, X)-isomorphism method, which is an anonymizing technique that produces for every subgraph a K -1 candidate subgraph. A (K X)-isomorphism depends on a range of methods that will help to make for every subgraph a ${K-1}$ similar subgraphs, like weighted community detection, graph density, weighted maximum common subgraph and bi-clustering methods. This research improves an MPD_V method which is a maximum common subgraph, where this improvement makes MPD_V more fitting to find the similarly weighted subgraphs." @default.
• W2907171162 created "2019-01-11" @default.
• W2907171162 creator A5023598867 @default.
• W2907171162 creator A5046462412 @default.
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• W2907171162 date "2018-04-01" @default.
• W2907171162 modified "2023-10-03" @default.
• W2907171162 title "A Novel (K, X)-isomorphism Method for Protecting Privacy in Weighted social Network" @default.
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• W2907171162 doi "https://doi.org/10.1109/ncg.2018.8593107" @default.
• W2907171162 hasPublicationYear "2018" @default.
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