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..
Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Authors: Xenia Miscouridou, Francois Caron, Yee Whye Teh
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on real-world temporal interaction data and show that the proposed model outperforms competing approaches for link prediction, and leads to interpretable parameters. and Section 6 presents experiments on four real-world temporal interaction datasets. |
| Researcher Affiliation | Collaboration | 1Department of Statistics, University of Oxford 2Deep Mind |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We use the MCMC scheme of (Todeschini et al., 2016) and the accompanying software package SNet OC2 to perform inference. 2https://github.com/misxenia/SNet OC |
| Open Datasets | Yes | We perform experiments on four temporal interaction datasets from the Stanford Large Network Dataset Collection3 (Leskovec and Krevl, 2014): The EMAIL dataset... The COLLEGE dataset... The MATH overflow dataset... The UBUNTU dataset... 3https://snap.stanford.edu/data/ |
| Dataset Splits | No | For each dataset, we make a train-test split in time so that the training datasets contains 85% of the total temporal interactions. (Explanation: The paper describes a train-test split but does not specify a separate validation set or its details.) |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using the software package 'SNet OC2' but does not provide a specific version number for it or other software dependencies. |
| Experiment Setup | Yes | The number of communities p is set to p = 4 for the EMAIL dataset... p = 2 for the COLLEGE dataset... and p = 3 for the MATH and UBUNTU datasets... We use 100000 iterations for the ο¬rst stage and 10000 for the second one. |