Modelling sparsity, heterogeneity, reciprocity and community structure in temporal interaction data
Authors: Xenia Miscouridou, Francois Caron, Yee Whye Teh
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | 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 first stage and 10000 for the second one. |