A Deterministic Partition Function Approximation for Exponential Random Graph Models
Authors: Wen Pu, Jaesik Choi, Yunseong Hwang, Eyal Amir
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our results show that the new method performs well experimentally comparing to existing sampling methods [Gelman and Meng, 1998; Handcock et al., 2003] on synthetic data and real-world social networks. |
| Researcher Affiliation | Collaboration | Wen Pu LinkedIn Corporation Mountain View, CA, USA wpu@linkedin.com Jaesik Choi Ulsan National Institute of Science and Technology, Ulsan, Korea jaesik@unist.ac.kr Yunseong Hwang Ulsan National Institute of Science and Technology, Ulsan, Korea yunseong@unist.ac.kr Eyal Amir University of Illinois at Urbana-Champaign Urbana, IL, USA eyal@illinois.edu |
| Pseudocode | Yes | Algorithm 1 Our new ECS Approximation to the log partition function ln Z(θ) Input: model parameter θ and number of nodes n Output: estimation of ln Z(θ) Initialize ECS for u 0 to n(n 1)/2 do ECS max{ γ(θ, u), ECS} end for |
| Open Source Code | No | No statement regarding the release of open-source code for the described methodology is provided, nor is a link to a code repository. |
| Open Datasets | Yes | To study the stability and quality of ECS-MLE results, we fit two different models with four networks with more than 40 nodes: one kapferer2 from statnet; and the other three networks, prison, dolphins, and sanjuansur, from CMU CASOS. |
| Dataset Splits | No | No specific training/test/validation splits are provided. The paper describes generating synthetic data and simulating graphs from models for comparison, but does not detail dataset partitioning for reproduction. |
| Hardware Specification | No | No specific hardware details (e.g., CPU, GPU models, memory, or cluster specifications) used for running the experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like 'statnet' and algorithms such as 'Bridge Sampling' and 'MCMC-MLE', but does not provide specific version numbers for any software dependencies required to replicate the experiments. |
| Experiment Setup | Yes | For ECS-MLE, we performed grid search in a slightly enlarged parameter space with finer granularity. For triad models, our ECS algorithm searched the grid of two parameters of (edge, 2-star, triangle) ranging the initial value plus offsets in [ -5, 5] x [ -5, 5] x [ -5, 5], with granularity of 0.2. For alternating k-stars, the ECS searched the grid of three parameters of (edge, altkstar) ranging, initial value plus offsets in [ -15, 15] x [ -15, 15], with the same granularity. |