Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Community Detection in Social Networks Considering Topic Correlations
Authors: Yingkui Wang, Di Jin, Katarzyna Musial, Jianwu Dang321-328
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 16,103 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,788 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our model is evaluated on two real datasets and is compared with four state-of-the-art methods. Experimental results show that TCCD significantly improves the accuracy of community detection. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin 300350, China, 2Advanced Analytics Institute, School of Software, University of Technology Sydney, Australia, 3School of Information Science, Japan Advanced Institute of Science and Technology, Japan |
| Pseudocode | Yes | Algorithm 1 Inference for TCCD |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | To accurately evaluate community detection results of TCCD and other baselines, we choose two real datasets with ground-truth: Reddit dataset and DBLP dataset (Wang, Lai, and Philip 2014). |
| Dataset Splits | No | The paper describes the datasets used (Reddit and DBLP) but does not provide specific training, validation, or test dataset splits (e.g., percentages, sample counts, or references to predefined splits). |
| Hardware Specification | Yes | All experiments are implemented on a computer with Intel 4.2GHz CPUs and 32GB RAMs. |
| Software Dependencies | No | The paper mentions 'Pre Tex T2' for stemming but does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks). |
| Experiment Setup | Yes | For Dirichlet hyperparameters, we run TCCD under different values. The results show that TCCD is not sensitive to Dirichlet hyperparameters, thus we set them to fixed values (i.e., ρ = 0.01, α = 0.001, β = 0.1, ϵ = 0.001). |