2014
A Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection
Lin W, Kuo T, Huang Y, Lu W, Lin S. A Transfer-Learning Approach to Exploit Noisy Information for Classification and Its Application on Sentiment Detection. Lecture Notes In Computer Science 2014, 8916: 262-273. DOI: 10.1007/978-3-319-13987-6_25.Peer-Reviewed Original Research
2013
Unsupervised link prediction using aggregative statistics on heterogeneous social networks
Kuo T, Yan R, Huang Y, Kung P, Lin S. Unsupervised link prediction using aggregative statistics on heterogeneous social networks. 2013, 775-783. DOI: 10.1145/2487575.2487614.Peer-Reviewed Original ResearchHeterogeneous social networksSocial networksUnsupervised link predictionFactor graph modelOnline social networksAggregate statisticsLabeled dataUnsupervised frameworkUnsupervised modelInference algorithmLink predictionOpinion holdersGraph modelNetworkDBLPNDCGPlurkPrediction scenariosScenariosPrivacyFoursquareUnsupervisedTwitterAlgorithmDataset
2011
Assessing the Quality of Diffusion Models using Real-World Social Network Data
Kuo T, Hung S, Lin W, Lin S, Peng T, Shih C. Assessing the Quality of Diffusion Models using Real-World Social Network Data. 2011, 200-205. DOI: 10.1109/taai.2011.42.Peer-Reviewed Original ResearchIndependent Cascade ModelMicro-blog dataLinear threshold modelMicro-blogInformation propagationReal worldSocial networksIndirect schemePlurkCascade modelEvaluation frameworkIn-degreeNetwork DataSchemeCascading phenomenaFrameworkDifferent circumstancesPageRankTwitterNetworkDataFacebookDiffusion eventsModelDiffusion model