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 [1].
Dynamic Network Model from Partial Observations
Authors: Elahe Ghalebi, Baharan Mirzasoleiman, Radu Grosu, Jure Leskovec
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show the effectiveness of our approach using extensive experiments on synthetic as well as real-world networks. |
| Researcher Affiliation | Academia | Elahe Ghalebi TU Wien EMAIL Baharan Mirzasoleiman Stanford University EMAIL Radu Grosu TU Wien EMAIL Jure Leskovec Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 EXTRACT_OBSERVATIONS Input: Set of cascades {tc1, , tc|C|}, sample size q. Output: Extracted multiset of edges X from cascades. ... Algorithm 2 UPDATE_NETWORK_MODEL Input: Model M(c1:M, p1:M, Ξ²1:N), set of cascades {tc1, , tc|C|}. Output: Updated model M (c1:M, p1:M, Ξ²1:N) ... Algorithm 3 DYNAMIC_NETWORK_INFERENCE (DYFERENCE) Input: Set of infection times {tc1, , tc|C|}, interval length w. Output: Updated network model Mt at times t = iw. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository. |
| Open Datasets | Yes | (1) Twitter [37] contains the diffusion of URLs on Twitter during 2010 and the follower graph of users. ... (2) Memes [38] contains the diffusion of memes from March 2011 to February 2012 over online news websites; |
| Dataset Splits | Yes | We use the infection times in the ο¬rst 80% of the total time interval for training, and the remaining 20% for the test. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | In all the experiments we use a sample size of q = |Ec| 1 for all the cascades c C. We further consider a window of length w = 1 day in our dynamic network inference experiments in Fig 1 and w = 2-years in Table 3. |