Abstract
Prediction of information propagation is an important issue in research of social network. Recent researches can be divided into graph or non-graph approaches. Most of non-graph approaches use regression analysis and probability model, seldomly considering clustering features of social time series. In clustering-based temporal prediction model, every cluster center is treated as a propagation pattern, and so that the prediction can be realized through classification to find out the nearest-neighbor pattern. Prediction performance may be influenced by clustering performance based on clustering approaches. This paper proposes a new model Scaling Clustering based Temporal Prediction Model (SCTPM), which is applicable for predicting propagation pattern of social information. Through 10-fold cross-validation experiments on twitter and phrase datasets, SCTPM obtains lower prediction bias and variance than the existing clustering-based models.
This work is supported by the Natioanl Science Foundation of China (Nos. 61572407 and 61603313), the Project of National Science and Technology Support Program (No. 2015BAH19F02).
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Teng, F., Tang, R., Yang, Y., Wang, H., Dai, R. (2017). Temporal Prediction Model for Social Information Propagation. In: Polkowski, L., et al. Rough Sets. IJCRS 2017. Lecture Notes in Computer Science(), vol 10313. Springer, Cham. https://doi.org/10.1007/978-3-319-60837-2_38
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DOI: https://doi.org/10.1007/978-3-319-60837-2_38
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