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].

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 | Venue PDF | 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 EMAIL Jakab Tardos EPFL EMAIL Mohammad Hossein Bateni Google Research EMAIL André Linhares Google Research EMAIL Filipe Miguel Gonçalves de Almeida Google Research filipea@google.com Andrea Montanari Stanford University EMAIL Ashkan Norouzi-Fard Google Research EMAIL
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.