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• W2912963151 abstract "Conditional random field (CRF) is a useful tool for many machine learning tasks, and numerous social network analysis jobs can be modeled by general CRFs. However, there are several problems obstructing applying general CRFs on online social network data mining tasks. For demonstration, we propose a unified CRF model for trust prediction to tackle the following problems: trust relationships are directed but existing work mostly deals with undirected links; online social networks are generally large and sparse, and traditional methods and their implementations perform poor on large datasets in both predictive accuracy and time efficiency. The proposed model has its unifications in three aspects: it models various types of entities in online social networks and captures the interconnection and interplay between them, it considers both directed links and undirected links, and it can embrace an arbitrary number of features to model the real-world online social networks. For better performance, our model is implemented with parallel computing support and gain further speedups by utilizing multiple GPU devices with massive computation powers. Experimental results show both the model's validity and the implementation's effectiveness." @default.
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• W2912963151 date "2018-06-01" @default.
• W2912963151 modified "2023-10-16" @default.
• W2912963151 title "A High Performance Implementation of A Unified CRF Model for Trust Prediction" @default.
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• W2912963151 doi "https://doi.org/10.1109/hpcc/smartcity/dss.2018.00141" @default.
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