Overlapping Clustering Models, and One (class) SVM to Bind Them All
Authors: Xueyu Mao, Purnamrita Sarkar, Deepayan Chakrabarti
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 xmao@cs.utexas.edu, purna.sarkar@austin.utexas.edu, deepay@utexas.edu |
| 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. |