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..
Fast Algorithms for Hypergraph PageRank with Applications to Semi-Supervised Learning
Authors: Konstantinos Ameranis, Adela Frances Depavia, Lorenzo Orecchia, Erasmo Tani
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In addition to giving strong theoretical guarantees, we empirically showcase the speed of our algorithms on benchmark instances of semi-supervised learning on categorical data. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of Chicago, Chicago, USA 2Computational and Applied Mathematics, University of Chicago, Chicago, USA. Correspondence to: Lorenzo Orecchia <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 with prox-generating function 1/2 2 R for min F(x) s, x in Theorem 4.1 |
| Open Source Code | Yes | Code for conducting all experiments in Appendix C and Appendix D is publically available on Git Hub.3 ... 3Full link address: https://github.com/Orecchia-Research-Group/hypergraph_diffusions |
| Open Datasets | Yes | As a benchmark, we adopt the semi-supervised multi-class classification task on a small set of UCI datasets (Kelly et al.) which was considered in previous works on the topic (Zhou et al., 2003; Hein et al., 2013; Li et al., 2020). |
| Dataset Splits | No | The paper describes using a small subset of labeled points for semi-supervised learning, but it does not provide specific training, validation, or test dataset splits (e.g., percentages or counts) for reproducibility. |
| Hardware Specification | Yes | All experiments were run on a server with a 24core Intel Xeon Silver 4116 CPU @ 2.10GHz processor and 128gb RAM. |
| Software Dependencies | No | The paper mentions C++ and MATLAB implementations but does not specify version numbers for these or any other software libraries or dependencies. |
| Experiment Setup | Yes | We use teleportation constant α = 0.5. For the hypergraph, we use Algorithm 1 with 50 iterations to compute the PPR vector... We chose this single value of λ as it proved representative of the relative behavior of each method. |