Fast and Accurate Spreading Process Temporal Scale Estimation
Authors: Abram Magner, Carolyn S Kaminski, Petko Bogdanov
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further evaluate the performance of Fast Clock empirically in comparison to the state of the art estimator from Di Tursi et al, 2017. We find that in a broad parameter range on synthetic networks and on a real network, our algorithm substantially outperforms that algorithm in terms of both running time and accuracy. In all cases, our algorithm s running time is asymptotically lower than that of the baseline. |
| Researcher Affiliation | Academia | Abram Magner EMAIL University at Albany, SUNY Carolyn Kaminski EMAIL University at Albany, SUNY Petko Bogdanov EMAIL University at Albany, SUNY |
| Pseudocode | Yes | Algorithm 1: Fast Clock |
| Open Source Code | No | The paper does not provide an explicit statement or link for the open-sourcing of their code for the methodology described. |
| Open Datasets | Yes | Table 1 lists the structural statistics for large online social networks often used in empirical network science research. Such networks are well within the expected density ranges we employ in our theoretical analysis, namely their average degree is in the order (and typically higher) than a natural logarithm of the number of nodes. SNAP (http: //snap.stanford.edu/data) |
| Dataset Splits | No | We draw 50 samples for each setting and report average and standard deviation for both running time and quality of estimations for each setting. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | default parameters for all experiments: pn = 0.1, pe = 10 7, n = 3000, p = n 1/3, stretch l = 2 unless varying in the specific experiment. |