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.
Overlapping Clustering Models, and One (class) SVM to Bind Them All
Authors: Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
NeurIPS 2018 | Venue PDF | LLM Run Details | Input Tokens: 18,455 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,155 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 | Experimental results on several simulated and real datasets show our algorithm (called SVM-cone) is both accurate and scalable. |
| Researcher Affiliation | Academia | Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti The University of Texas at Austin EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 SVM-cone |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code for the SVM-cone method, nor does it include a link to a code repository. |
| Open Datasets | Yes | For networks, we used the 5 DBLP coauthorship networks1 (used in [20], where each ground truth community corresponds to a group of conferences on the same topic. We also use bipartite author-paper variants for these 5 networks. 1http://www.cs.utexas.edu/~xmao/coauthorship |
| Dataset Splits | No | The paper describes dataset generation parameters (e.g., 'We generate networks with n = 5000 nodes and K = 3 communities.') and sampling parameters, but it does not specify explicit training, validation, or test dataset splits needed for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU models, CPU types, or memory specifications) used for running the experiments. |
| Software Dependencies | Yes | We use Matlab R2018a built-in Gibbs Sampling function for learning topic models to learn the word by topic matrix, which should retain the characteristics of real data distributions. |
| Experiment Setup | Yes | We set Bii = 1 and Bij = 0.1 for all i = j. The default degree parameters for DCMMSB are as follows: for all nodes i that are predominantly in the j-th community (θij > 0.5), we set Γii to 0.3, 0.5, and 0.7 for the 3 respective communities; all other nodes have Γii = 1. |