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 | Conference PDF | Archive PDF | Plain Text | 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, schariya@illinois.edu 2Adobe Research, {ryrossi,anuprao,tumai,lipka,zsong}@adobe.com 3University of Massachusetts Amherst, cmusco@cs.umass.edu |
| 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 (Pérez & 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 λ = 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. |