Streaming Belief Propagation for Community Detection
Authors: Yuchen Wu, Jakab Tardos, Mohammadhossein Bateni, André Linhares, Filipe Miguel Goncalves de Almeida, Andrea Montanari, Ashkan Norouzi-Fard
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate our theoretical findings on synthetic and real data. and 5 Empirical evaluation |
| Researcher Affiliation | Collaboration | Yuchen Wu Stanford University wuyc14@stanford.edu Jakab Tardos EPFL jakab.tardos@epfl.ch Mohammad Hossein Bateni Google Research bateni@google.com André Linhares Google Research linhares@google.com Filipe Miguel Gonçalves de Almeida Google Research filipea@google.com Andrea Montanari Stanford University montanari@stanford.edu Ashkan Norouzi-Fard Google Research ashkannorouzi@google.com |
| Pseudocode | Yes | Algorithm 1 Streaming R-local belief propagation and Algorithm 2 STREAMBP : Bounded-distance streaming BP |
| Open Source Code | No | The paper does not explicitly state that source code for the described methodology is available, nor does it provide a link to a code repository. |
| Open Datasets | Yes | Cora [RA15], Citeseer [RA15], and Polblogs [AG05]. |
| Dataset Splits | No | The paper uses synthetic and real-world datasets but does not explicitly provide details on training, validation, and test dataset splits or the methodology for creating them. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We use various settings for k, a, b, α. and Figure 3 captions (e.g., k = 2, a = 3, b = .1, α = .4.) show specific parameter values. |