Summary of On Your Mark, Get Set, Predict! Modeling Continuous-time Dynamics Of Cascades For Information Popularity Prediction, by Xin Jing et al.
On Your Mark, Get Set, Predict! Modeling Continuous-Time Dynamics of Cascades for Information Popularity Prediction
by Xin Jing, Yichen Jing, Yuhuan Lu, Bangchao Deng, Sikun Yang, Dingqi Yang
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel approach to predict information popularity in various domains, such as viral marketing and news recommendations. The proposed method, called ConCat, models the continuous-time dynamics of cascades using neural Ordinary Differential Equations (ODEs) and neural temporal point processes (TPPs). ConCat leverages the cascade graph and sequential event information to capture irregular events and complex diffusion processes. Compared to existing methods, such as recurrent networks and self-exciting point processes, ConCat achieves superior performance on three real-world datasets, with a 2.3%-33.2% improvement over the best-performing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about predicting how popular information will be. It’s like trying to guess which news article will get shared most online. The authors came up with a new way to do this called ConCat. They used special math formulas to understand how information spreads and changes over time. This helps them make more accurate predictions than other methods that tried to do the same thing. |
Keywords
» Artificial intelligence » Diffusion
