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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
Edge-exchangeable graphs and sparsity
Authors: Diana Cai, Trevor Campbell, Tamara Broderick
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We use these models to show that edge-exchangeable models can yield sparse, projective graph sequences via theoretical analysis in Section 5 and via simulations in Section 6. In this section, we explore the behavior of graphs generated by the model from Section 5 via simulation, with the primary goal of empirically demonstrating that the model produces sparse graphs. |
| Researcher Affiliation | Academia | Diana Cai Dept. of Statistics, U. Chicago Chicago, IL 60637 EMAIL Trevor Campbell CSAIL, MIT Cambridge, MA 02139 EMAIL Tamara Broderick CSAIL, MIT Cambridge, MA 02139 EMAIL |
| Pseudocode | No | The paper describes methods using natural language and mathematical equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code for the described methodology, nor does it include a link to a code repository. |
| Open Datasets | No | The paper does not use a pre-existing, publicly available dataset. Instead, it generates data through simulations based on a theoretical model, as described: 'We consider the case when the Poisson process generating the weights in Equation (2) has the rate measure of a three-parameter beta process (3-BP) on (0, 1)...' |
| Dataset Splits | No | The paper describes simulations where data is generated rather than using a pre-existing dataset with defined training, validation, or test splits. Therefore, no such split information is provided. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the simulations or experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies or version numbers used for the implementation or simulations. |
| Experiment Setup | Yes | The parameters of the beta process were fixed to γ = 3 and θ = 1, as they do not influence the sparsity of the resulting graph frequency model, and we varied the discount parameter α. Given a single draw W (at some specific discount α), we then simulated the edges of the graph, where the number of Bernoulli draws N varied between 50 and 2000. |