Open AccessDistrust Prediction in Signed Social Network
Author(s) -
Shen Pengfei,
Liu Shufen,
Han Lu
Publication year - 2019
Publication title -
chinese journal of electronics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.267
H-Index - 25
eISSN - 2075-5597
pISSN - 1022-4653
DOI - 10.1049/cje.2018.11.005
Subject(s) - distrust , computer science , social network (sociolinguistics) , relation (database) , process (computing) , social relation , social network analysis , machine learning , artificial intelligence , data mining , social psychology , psychology , social media , world wide web , operating system , psychotherapist
In social networks, the most studies focus on the trust prediction, but distrust cannot get enough attention. The distinct characteristics of distrust relations present challenges to traditional relation prediction. Distrust relations are very sparse in social network, and negative interaction data is too little. We embark on the problem to investigate the distrust prediction with only network topology. After achieving seven social distrusting‐inducing factors, we adopt machine learning and optimization methods to model the prediction process. The framework of Distrust prediction in Signed social network (DP‐SSN) is proposed, which can predict distrust relations without any interaction data. Empirically, we perform extensive experiments on real‐world data to corroborate the effectiveness of the proposed framework.