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..
Exact Representation of Sparse Networks with Symmetric Nonnegative Embeddings
Authors: Sudhanshu Chanpuriya, Ryan Rossi, Anup B. Rao, Tung Mai, Nedim Lipka, Zhao Song, Cameron Musco
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments on real-world networks, we demonstrate our factorization s effectiveness on a variety of tasks, including community detection and link prediction. ... 6 Experiments ... 6.3 Results |
| Researcher Affiliation | Collaboration | Sudhanshu Chanpuriya1, Ryan A. Rossi2, Anup Rao2, Tung Mai2, Nedim Lipka2, Zhao Song2, and Cameron Musco3 1University of Illinois Urbana-Champaign, EMAIL 2Adobe Research, EMAIL 3University of Massachusetts Amherst, EMAIL |
| Pseudocode | Yes | Algorithm 1 Converting LPCA Factors to Community Factors ... Algorithm 2 Fitting the Constrained Model |
| Open Source Code | Yes | We include code in the form of a Jupyter notebook (Pรฉrez & Granger, 2007) demo. |
| Open Datasets | Yes | We use five fairly common mid-size datasets ranging from around 1K to 10K nodes. ... BLOG Tang & Liu (2009) ... YOUTUBE Yang & Leskovec (2015) ... POS Qiu et al. (2018) ... PPI Breitkreutz et al. (2007) ... AMAZON Yang & Leskovec (2015) |
| Dataset Splits | No | The paper describes a 90% training / 10% test split for link prediction but does not specify a separate validation split for general model training or explicit percentages for all dataset splits needed for full reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using PyTorch and SciPy, but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set regularization weight ฮป = 10 as in Yang & Leskovec (2013). ... up to a max of 200 iterations of optimization. ... We generally use a batch size of 100; we find that the optimization of BIGCLAM often diverges on COMPANY B with this batch size, so we instead use batches of size 1000 for its optimization. |