Research map / Learning theory and algorithms / Prediction and decision losses
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use TDA instead of 10^4-sample Monte Carlo sampling | cascade size distribution estimation · Random graphs, varying network size | runtime: 68-485 | Helped |
| Use TDA instead of sampling | Tree (N=181) · Tree (N=181) | runtime: 18790 times faster | Helped |
| Use TDA instead of sampling | YouTube network · YouTube network (N=1134890) | runtime: almost 2 times faster | Helped |
| Apply TDA to the corporate ownership network | TDA · Corporate ownership network (N=4475) | cascade size distribution approximation quality | Helped |
| Apply TDA to the dense cancer network | TDA · Cancer miRNA network (N=201) | cascade size distribution approximation quality | Helped |
| Use BP-estimated p_ij in TDA on YouTube | TDA · YouTube network (N=1134890) | average cascade size | Hurt |
| Use p_ij = p_i and shift the cascade size distribution using 100 samples | TDA on YouTube · YouTube network (N=1134890) | average cascade size: 100 | Helped |
| Increase the average degree of the network | TDA · Random graphs, varying average degree | cascade size distribution approximation quality | Mixed |
| Increase clustering coefficient while keeping average degree small | TDA · Random graphs, varying clustering coefficient | cascade size distribution approximation quality | No clear effect |
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