Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions
Authors: Shubham Gupta, Ambedkar Dukkipati
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, fourth, we present empirical results on both real and simulated data to corroborate our theoretical findings (Section 5). In particular, our experiments show that our algorithms perform well in practice, even when the d-regularity assumption on R is violated. |
| Researcher Affiliation | Collaboration | Shubham Gupta IBM Research Paris-Saclay Orsay 91400, France. shubhamg@iisc.ac.in Ambedkar Dukkipati Computer Science and Automation Indian Institute of Science, Bangalore, India. ambedkar@iisc.ac.in |
| Pseudocode | Yes | Algorithm 1 Unnormalized spectral clustering ... Algorithm 2 UREPSC |
| Open Source Code | Yes | Code submitted as supplemental material. |
| Open Datasets | Yes | A real-world network: The FAO trade network from United Nations is a multiplex network with 214 nodes representing countries and 364 layers representing commodities like coffee and barley [Domenico et al., 2015]. |
| Dataset Splits | No | The paper describes the generation of synthetic data and the construction of graphs from a real-world network, but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or counts) as commonly used in machine learning for model development and evaluation. |
| Hardware Specification | No | The experiments were run on a standard desktop and did not require any specialized hardware. |
| Software Dependencies | No | The paper mentions software like MATLAB and scikit-learn in the context of k-means clustering algorithms but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We sampled similarity graphs from R-PP using p = 0.4, q = 0.3, r = 0.2, and s = 0.1 for various values of d, N, and K, while ensuring that the underlying R satisfies Assumption 4.1 and rank{R} N k. ... In all cases, we use R = P = N/10. ... We connect each node to its five nearest neighbors in each layer and make the edges undirected. |