Information-theoretic Limits for Community Detection in Network Models
Authors: Chuyang Ke, Jean Honorio
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the information-theoretic limits for the recovery of node labels in several network models. This includes the Stochastic Block Model, the Exponential Random Graph Model, the Latent Space Model, the Directed Preferential Attachment Model, and the Directed Small-world Model. For the Stochastic Block Model, the non-recoverability condition depends on the probabilities of having edges inside a community, and between different communities. For the Latent Space Model, the non-recoverability condition depends on the dimension of the latent space, and how far and spread are the communities in the latent space. For the Directed Preferential Attachment Model and the Directed Small-world Model, the non-recoverability condition depends on the ratio between homophily and neighborhood size. We also consider dynamic versions of the Stochastic Block Model and the Latent Space Model. |
| Researcher Affiliation | Academia | Chuyang Ke Department of Computer Science Purdue University West Lafayette, IN 47907 cke@purdue.edu Jean Honorio Department of Computer Science Purdue University West Lafayette, IN 47907 jhonorio@purdue.edu |
| Pseudocode | Yes | Algorithm 1: k-simplex |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper is theoretical, focusing on information-theoretic limits and proving theorems. It does not involve any empirical training on datasets. |
| Dataset Splits | No | The paper is theoretical and does not involve dataset splits (training, validation, or test) as it does not conduct empirical experiments. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require specific hardware specifications for reproduction. |
| Software Dependencies | No | The paper is theoretical and does not mention any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, including hyperparameters or system-level training settings. |