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..
One-Shot Compression of Large Edge-Exchangeable Graphs using Bits-Back Coding
Authors: Daniel Severo, James Townsend, Ashish J Khisti, Alireza Makhzani
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we showcase the optimality of REC on large graphs representing real-world networks. We entropy code with REC using P olya s Urn (PU) model and compare the performance to state-of-the-art compression algorithms tailored to network compression. We report the average number of bits required to represent an edge in the graph (i.e., bits-per-edge) as is common in the literature. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, University of Toronto 2Vector Institute for Artificial Inteligence 3Amsterdam Machine Learning Lab (AMLab), University of Amsterdam. |
| Pseudocode | Yes | Algorithm 1 Naive Random Edge Encoder and Algorithm 2 Random Edge Encoder |
| Open Source Code | Yes | Code implementing Random Edge Coding, P olya s Urn model, and experiments are available at https:// github.com/dsevero/Random-Edge-Coding. |
| Open Datasets | Yes | We used datasets containing simple network graphs with small-world statistics (see Section 4.1) such as You Tube, Four Square, Gowalla, and Digg (Rossi & Ahmed, 2015)... Skitter and DBLP networks (Leskovec & Krevl, 2014) |
| Dataset Splits | No | The paper describes compressing the entire graph in a one-shot manner and does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running experiments, such as specific GPU or CPU models. |
| Software Dependencies | No | We use the ANS implementation available in Craystack (Townsend et al., 2020; 2021). However, specific version numbers for Craystack or other software dependencies are not provided. |
| Experiment Setup | No | The paper describes the general process of Random Edge Coding and Polya's Urn model, which is parameter-free, but does not provide specific experimental setup details like hyperparameters or training configurations for a learning model. |