Open AccessProceedings Article
Modeling and predicting popularity dynamics via reinforced Poisson processes
Huawei Shen,Dashun Wang,Chaoming Song,Albert-László Barabási +3 more
- 27 Jul 2014
- Vol. 28, Iss: 1, pp 291-297
TL;DR: A generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity and its remarkable power at predicting the popularity of individual items is proposed.
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Abstract: An ability to predict the popularity dynamics of individual items within a complex evolving system has important implications in an array of areas. Here we propose a generative probabilistic framework using a reinforced Poisson process to explicitly model the process through which individual items gain their popularity. This model distinguishes itself from existing models via its capability of modeling the arrival process of popularity and its remarkable power at predicting the popularity of individual items. It possesses the flexibility of applying Bayesian treatment to further improve the predictive power using a conjugate prior. Extensive experiments on a longitudinal citation dataset demonstrate that this model consistently outperforms existing popularity prediction methods.
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Comment on “Quantifying long-term scientific impact”
Jian Wang,Yajun Mei,Diana Hicks +2 more
TL;DR: Results are discouraging: 14.75% papers are estimated with unreasonably large μ (>5) and λ (>10) and correspondingly enormous prediction errors and the prediction power is even worse than simply using short-term citations to approximate long- term citations.
29
Response to Comment on “Quantifying long-term scientific impact”
TL;DR: It is shown that the model provides an effective means to identify papers that may be subject to overfitting, and the model, with or without prior treatment, outperforms the proposed naïve approach.
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