Modeling Random Networks with Heterogeneous Reciprocity
Authors: Daniel Cirkovic, Tiandong Wang
JMLR 2024 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We compare Bayesian and frequentist model fitting techniques for large networks, as well as computationally efficient variational alternatives. Cases where the number of communities is known and unknown are both considered. We apply the presented methods to the analysis of Facebook and Reddit networks where users have nonuniform reciprocal behavior patterns. |
| Researcher Affiliation | Academia | Daniel Cirkovic EMAIL Department of Statistics Texas A&M University College Station, TX 77843, USA, Tiandong Wang EMAIL Shanghai Center for Mathematical Sciences Fudan University Shanghai 200438, China |
| Pseudocode | Yes | Algorithm 1 Gibbs sampling for heterogeneous reciprocal PA with known K, Algorithm 2 CAVI for heterogeneous reciprocal PA with known K, Algorithm 3 VEM for heterogeneous reciprocal PA with known K, Algorithm 4 Telescoping sampler for heterogeneous reciprocal PA with known K, Algorithm 5 Initialization of VEM for heterogeneous reciprocal PA |
| Open Source Code | No | The paper does not contain any explicit statement about providing access to the source code for the methodology described, nor does it provide a direct link to a code repository. |
| Open Datasets | Yes | We apply the presented methods to the analysis of Facebook and Reddit networks where users have nonuniform reciprocal behavior patterns. Now we apply the heterogeneous reciprocal PA model to the Facebook wall post data from KONECT analyzed in Viswanath et al. (2009) and Cirkovic et al. (2023a). We additionally analyze Reddit user replies from December 11th, 2005 to December 31st, 2006 (Hessel et al., 2016; Liu et al., 2019). |
| Dataset Splits | No | The paper describes trimming and processing the datasets, e.g., 'This trimming procedure results in a connected network of 16,099 nodes and 123,920 edges...', but does not specify any training, validation, or test splits for experimental reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'R s mcmcse package (Flegal et al., 2017)' and 'R package igraph (Csardi et al., 2006)', but does not provide specific version numbers for these or other ancillary software components. |
| Experiment Setup | Yes | For the VEM algorithm, we terminate the E-step once either the ELBO has increased by less than ϵ = 0.01 or the total number of iterations exceeds 500, and terminate the entire algorithm once the element-wise differences in the parameters fall below κ = 0.01. We also terminate the VB algorithm via the same conditions as in the E-step of the VEM algorithm. We run the fully Bayesian method for M = 5,000 MCMC samples, and discard the first half as burn-in. Further, when K is unknown, we assume a BNB(1, 4, 3) prior on K as recommended by Fr uhwirth-Schnatter et al. (2021) and set Kmax = 20. For the variational methods, we search over K = 1, 2, 3, 4. |