Reconstructing Parameters of Spreading Models from Partial Observations
Authors: Andrey Lokhov
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the performance of the DMPREC algorithm on synthetic and real-world networks under assumption of partial observations. In numerical experiments, we focus primarily on the presence of inaccessible nodes, which is a more computationally difficult case compared to the setting of missing information in time. |
| Researcher Affiliation | Academia | Andrey Y. Lokhov Center for Nonlinear Studies and Theoretical Division T-4 Los Alamos National Laboratory, Los Alamos, NM 87545, USA lokhov@lanl.gov |
| Pseudocode | No | The paper describes the dynamic message-passing equations and their derivatives but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any explicit statements about the release of source code or links to a code repository for the described methodology. |
| Open Datasets | Yes | In the tests described in this section... a real directed network of relationships in a New England monastery with N = 18 nodes [24]. ... used the data provided by the Bureau of Transportation Statistics [25]... http://www.rita.dot.gov/bts/. |
| Dataset Splits | No | The paper describes experiments with generated cascades and real-world network data but does not specify explicit training, validation, and test dataset splits with percentages or sample counts. |
| Hardware Specification | No | The paper mentions running computations 'on a standard laptop' but does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for the experiments. |
| Software Dependencies | No | The paper mentions 'Gephi software' for visualization but does not provide specific version numbers for it or any other software dependencies used in the experiments. |
| Experiment Setup | Yes | In the tests described in this section, the couplings {αij} are sampled uniformly in the range [0, 1], the final observation time is set to T = 10. ... Both algorithms are initialized with αij = 0.5 for all (ij) E. |