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..
Node Embeddings and Exact Low-Rank Representations of Complex Networks
Authors: Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, Charalampos Tsourakakis
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we perform a large number of experiments that verify the ability of very low-dimensional embeddings to capture local structure in real-world networks. |
| Researcher Affiliation | Academia | Sudhanshu Chanpuriya University of Massachusetts Amherst EMAIL Cameron Musco University of Massachusetts Amherst EMAIL Charalampos E. Tsourakakis Boston University & ISI Foundation EMAIL Konstantinos Sotiropoulos Boston University EMAIL |
| Pseudocode | No | The paper describes algorithms in text but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code is available at https://github.com/schariya/ exact-embeddings. |
| Open Datasets | Yes | Our evaluations are based on 11 popular real-network datasets, detailed below. Table 2 lists and shows some statistics of these datasets. For all networks, we ignore weights (setting non-zero weights to 1) and remove self-loops where applicable. PROTEIN-PROTEIN INTERACTION (PPI) [SBCA+10]... WIKIPEDIA [GL16]... BLOGCATALOG [ALM+09]... FACEBOOK [LM12]... CA-HEPPH and CA-GRQC [LKF07]... PUBMED [NLG+12]... P2P-GNUTELLA04 [LKF07]... WIKI-VOTE [LHK10]... CITESEER [SNB+08]... CORA [SNB+08] |
| Dataset Splits | No | The paper evaluates the ability to reconstruct network structure but does not explicitly describe training, validation, and test dataset splits in the context of model training for a predictive task. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Sci Py' but does not provide a specific version number. No other software dependencies are listed with their version numbers. |
| Experiment Setup | Yes | We initialize elements of the factors X, Y independently and uniformly at random on [ 1, +1]. We find factors that approximately minimize the loss using the Sci Py [JOP+ ] implementation of the L-BFGS [LN89, ZBLN97] algorithm with default hyper-parameters and up to a maximum of 2000 iterations. |