Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Authors: Mateusz Wilinski, Andrey Lokhov
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we test the SLICER algorithm on synthetic data. We start by presenting a comparison with established methods, and then empirically evaluate the performance of our algorithm on a variety of topologies, including random graphs, as well as regular lattices and real-world networks with a very large number of loops. |
| Researcher Affiliation | Academia | 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, USA. |
| Pseudocode | No | The complete form of the algorithm along with the full derivation is presented in the Supplementary Materials, section S1 (Wilinski & Lokhov, 2020), where we also discuss the selection of ε and its relation to the convergence of our learning procedure. This implies that the pseudocode is not in the main paper. |
| Open Source Code | Yes | A full implementation of our algorithms is available at (Wilinski & Lokhov, 2021), whereas the supplementary materials can be found in (Wilinski & Lokhov, 2020). |
| Open Datasets | Yes | We use two web networks for numerical tests: one representing the snapshot of the structure of the Internet at the level of autonomous systems (Rossi & Ahmed, 2015), and the other obtained by a web-crawler (Boldi et al., 2004). |
| Dataset Splits | No | The paper does not provide specific train/validation/test dataset splits with percentages or counts. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | For the optimization of maximum likelihood, we use the optimization software Ipopt (W achter & Biegler, 2006) within the Julia/Ju MP modeling framework for mathematical optimization (Dunning et al., 2017). No specific version numbers for these software components are provided. |
| Experiment Setup | Yes | We initialise the learning process with αij = 0.5 (ij) E, unless stated otherwise. In all the tests below, unless stated otherwise, (i) parameters αij are sampled uniformly from [0, 1]; (ii) each cascade is generated independently from the IC model with limited T, varying from 4 to 20; (iii) the source of every cascade is a single, randomly chosen, node and (iv) hidden nodes are chosen uniformly at random. |