Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Prediction-Centric Learning of Independent Cascade Dynamics from Partial Observations
Authors: Mateusz Wilinski, Andrey Lokhov
ICML 2021 | Venue PDF | 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. |