Hybrid-order Stochastic Block Model
Authors: Xunxun Wu, Chang-Dong Wang, Pengfei Jiao4470-4477
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on six real-world datasets show that the proposed method outperforms the existing approaches. |
| Researcher Affiliation | Academia | Xunxun Wu,1 Chang-Dong Wang, 1,2,3* Pengfei Jiao 4 1 School of Computer Science and Engineering, Sun Yat-sen University, Guangzhou, China 2 Guangdong Province Key Laboratory of Computational Science, Guangzhou, China 3 Key Laboratory of Machine Intelligence and Advanced Computing, Ministry of Education, China 4 Center of Biosafety Research and Strategy, Law school of Tianjin University, Tianjin, China |
| Pseudocode | Yes | Algorithm 1 Hybrid-order Stochastic Block Model |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | Six widely used real-world datasets are adopted to test the effectiveness of the proposed method. Karete Club1: A social network about karate clubs composed of 34 nodes and 78 edges, and the nodes are divided into 2 communities. Polbooks1: A book network about US politics composed of 105 nodes and 441 edges, and the nodes are divided into 3 communities. Polblogs1: A network of hyperlinks between weblogs on US politics composed of 1490 nodes and 19090 edges, and the nodes are divided into 2 communities. Dolphins1: A dolphin social network of frequent associations composed of 62 nodes and 159 edges, and the nodes are divided into 2 communities. Football1: A social network about the American college football league composed of 115 nodes and 616 edges, and the nodes are divided into 12 communities. DBLP2: A paper cooperative network, which is a subset of DBLP dataset, containing 1163 nodes and 1392 edges, and the nodes are divided into 3 communities. 1http://www-personal.umich.edu/ mejn/netdata/ 2http://snap.stanford.edu/data/ |
| Dataset Splits | No | The paper mentions 'ground-truth communities' for evaluation metrics like NMI and F1-Score, and discusses 'Extensive experiments on six real-world datasets' but does not specify details regarding training, validation, or testing splits (e.g., percentages, methodology, or specific files). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software components, libraries, or solvers used in the experiments. |
| Experiment Setup | No | The paper mentions proposing a 'heuristic algorithm to optimize the objective function' but does not provide specific details on hyperparameters, initialization strategies, or system-level training configurations necessary for reproduction. |