Recovering Communities in the General Stochastic Block Model Without Knowing the Parameters
Authors: Emmanuel Abbe, Colin Sandon
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We tested a simplified version of our algorithm on real data (see [28]), for the blog network of Adamic and Glance 05. We obtained an error rate of about 60/1222 (best trial was 57, worst 67), achieving the state-of-the-art (as described in [32]). |
| Researcher Affiliation | Academia | Emmanuel Abbe Department of Electrical Engineering and PACM Princeton University Princeton, NJ 08540 eabbe@princeton.edu Colin Sandon Department of Mathematics Princeton University Princeton, NJ 08540 sandon@princeton.edu |
| Pseudocode | Yes | 3.1.1 Simplified version of the algorithm for the symmetric case... 1. Set r = 3/4 log n/ log d and put each of the graph s edges in E with probability 1/10. 2. Set kmax = 1/δ and select kmax ln(4kmax) random vertices, v1, ..., vkmax ln(4kmax). 3. Compute Ir,r [E](vi vj) for each i and j. 4. If there is a possible assignment... 5. For every v in the graph, guess that v is in the same community as the v[i] that maximizes the value of Ir,r [E](v[i] v ). Also, "The Agnostic-degree-profiling algorithm. The inputs are (G, γ)... (1) Define the graph g... (2) Run Agnostic-sphere-comparison... (3) Determine the size... (4) For each node v... (5) Use σ v to get new estimates... (6) For each node v..." |
| Open Source Code | No | The paper does not provide any concrete access to source code, such as a repository link or an explicit statement about code release. |
| Open Datasets | Yes | We tested a simplified version of our algorithm on real data (see [28]), for the blog network of Adamic and Glance 05. |
| Dataset Splits | No | The paper mentions testing on real data but does not provide specific dataset split information (e.g., percentages or counts) for training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, or specific solvers used. |
| Experiment Setup | No | The paper describes algorithms and tests them on real data, but it does not provide specific experimental setup details such as concrete hyperparameter values, training configurations, or system-level settings. |