Exploiting Tradeoffs for Exact Recovery in Heterogeneous Stochastic Block Models
Authors: Amin Jalali, Qiyang Han, Ioana Dumitriu, Maryam Fazel
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we consider the SBM in its full generality, where there is no restriction on the number and sizes of communities or how they grow with the number of nodes, as well as on the connectivity probabilities inside or across communities. For such stochastic block models, we provide guarantees for exact recovery via a semidefinite program as well as upper and lower bounds on SBM parameters for exact recoverability. Our results exploit the tradeoffs among the various parameters of heterogenous SBM and provide recovery guarantees for many new interesting SBM configurations. |
| Researcher Affiliation | Academia | Amin Jalali Department of Electrical Engineering University of Washington Seattle, WA 98195 amjalali@uw.edu Qiyang Han Department of Statistics University of Washington Seattle, WA 98195 royhan@uw.edu Ioana Dumitriu Department of Mathematics University of Washington Seattle, WA 98195 dumitriu@uw.edu Maryam Fazel Department of Electrical Engineering University of Washington Seattle, WA 98195 mfazel@uw.edu |
| Pseudocode | No | The paper describes mathematical models, theorems, and algorithms in text and equations, but it does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | This paper is theoretical and focuses on mathematical models and proofs for exact recovery in Stochastic Block Models, rather than empirical experiments with datasets. Therefore, it does not discuss training data or its public availability. |
| Dataset Splits | No | This paper is theoretical and focuses on mathematical models and proofs for exact recovery in Stochastic Block Models, rather than empirical experiments with datasets. Therefore, it does not discuss validation dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not describe any experimental setup or the specific hardware used to run experiments. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers needed to replicate any experiments. |
| Experiment Setup | No | This paper is theoretical and focuses on mathematical proofs and conditions for community detection, not on practical experimental setups with hyperparameters or training configurations. |