Rise and fall patterns of information diffusion: model and implications
By Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos. Knowledge Discovery and Data Mining, 2012
358 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/9f76ffc30f4b16fa2b33e398ce4d750e603763ab
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Yi-Cheng Zhang
- Chaoming Song
- Dashun Wang
- Christos Faloutsos
- Albert-László Barabási
- Jan Gasthaus
- Huawei Shen
- Ron A. Boschma
In Statistical and Nonlinear Physics
- Jie Tang
- Yuxiao Dong
- Kyomin Jung
- Kuansan Wang
- Christos Faloutsos
- Yi-Cheng Zhang
- Dashun Wang
- Sejeong Kwon
Citations in the corpus
- Prominent Features of Rumor Propagation in Online Social Media (2013, background): “recently introduced the time series fitting model to describe the temporal pattern of a viral event with a diffusion mechanism and periodicity [8], We extend their model to better describe the temporal fluctuations of rumors and introduce a new model with additional features (e.”
- Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes (2014, methodology): “Among them, the so-called self-excited Hawkes conditional Poisson process has been used successfully to model the power-law relaxation of popularity dynamics and represents a promising predictive power [17].”
- Forecasting Big Time Series: Old and New (2018, background): “Time series data occur naturally in countless domains including medical analysis [24], sensor network monitoring [27], financial analysis [43], social activity mining [23, 22] and database systems [35, 19].”
- Epidemiological modeling of news and rumors on Twitter (2013, background): “[10] conducted research on the rise and fall patterns of information diffusion, and managed to capture the power-law fall pattern and periodicities inherent in such data.”
- Dynamics of information diffusion and its applications on complex networks (2016, background): “[82] presented a SPIKEMmodel, which can describe all the patterns of the rise-and-fall burst in the data collected from the public domain.”
- DeepInf: Social Influence Prediction with Deep Learning (2018, methodology): “, the rise-and-fall patterns [32], external influence sources [33], and conformity phenomenon [43].”