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..
Local Differential Privacy-Preserving Spectral Clustering for General Graphs
Authors: Sayan Mukherjee, Vorapong Suppakitpaisarn
TMLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical tests further corroborate these theoretical findings. |
| Researcher Affiliation | Academia | Sayan Mukherjee EMAIL The University of Tokyo Vorapong Suppakitpaisarn EMAIL The University of Tokyo |
| Pseudocode | Yes | Compute the second smallest eigenvector v = [v1, . . . , vn] of LG using the Lanczos algorithm or an approximation method, such as the one proposed in Adil & Saranurak (2024). Let v1, . . . , vn be distinct nodes of V such that vv1 vvn. Return the cut (S, S), where S = {v1, . . . , i0} and i0 = arg min 1 i n ÎąG(v1, . . . , vi). |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | Yes | We conduct experiments on real social networks to verify our theoretical results. In this work, we mainly use the network called Social circles: Facebook obtained from the Stanford network analysis project (SNAP), detailed in Leskovec & Mcauley (2012). |
| Dataset Splits | Yes | For each probability p and graph, we create 100 random graphs F with the given probability. |
| Hardware Specification | No | The paper does not contain any specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We examine p {0.0001q : 1 q 50}. For each probability p and graph, we create 100 random graphs F with the given probability. Note that the original graph is represented by G. We then compute the difference between the clustering results of G (represented by SC2(G)) and that of G F (represented by SC2(G F)). |