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 [1].
Exact Community Recovery under Side Information: Optimality of Spectral Algorithms
Authors: Julia Gaudio, Nirmit Joshi
ICLR 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also run simulations to verify our theoretical results (see Figure 2 and Appendix A.1). ... Lighter pixels correspond to higher rate of success. The blue and red curves are theoretical thresholds with and without side information respectively. |
| Researcher Affiliation | Collaboration | Julia Gaudio Northwestern University EMAIL Nirmit Joshi Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | Yes | Algorithm 1 An informal sketch of the spectral algorithm Algorithm 2 Spectral recovery algorithm for ROS, without or with side information. Algorithm 3 Degree-Profiling algorithm for ROS in the presence of BEC or BSC side information. Algorithm 4 Find Linear Combination Coefficients Algorithm 5 Spectral recovery algorithm for SBM (Rank-2) Algorithm 6 (Spectral) Recovery algorithm for SBM (Rank-1) Algorithm 7 Degree-Profiling algorithm for SBM in the presence of BEC or BSC side information. |
| Open Source Code | No | The paper does not explicitly state that source code for the current work is being released, nor does it provide a direct link to a code repository. It references implementation details in previous works but not a release for this paper. |
| Open Datasets | No | The paper uses models like Stochastic Block Model (SBM) and Z2-Synchronization to generate synthetic data for simulations, rather than relying on or providing access to pre-existing publicly available datasets. For example, it states: "Empirical Validations. In Figure 2, we consider the Z2-Synchronization setting..." |
| Dataset Splits | No | The paper uses simulated data based on theoretical models and does not describe traditional dataset splits (e.g., train/test/validation percentages or counts). The experimental setup involves generating data for a given 'n' and varying model parameters, not splitting a fixed dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the simulations. |
| Software Dependencies | No | The paper does not explicitly mention any software or library dependencies with version numbers. |
| Experiment Setup | Yes | In Figure 2, we consider the Z2-Synchronization model which is ROSn(1/2, a, a). With n = 300, for each type of side information channel of strength β (see Appendix A.1), we validate the performance of the spectral algorithm over N = 50 trails over a grid of values for (β, a). ... For the symmetric SBM, i.e. SBMn(1/2, a, b). We let n = 300 and consider the BEC side information channel with ϵ = n β for β {0, 5, 0.7}... |