Self-Guided Community Detection on Networks with Missing Edges
Authors: Dongxiao He, Shuai Li, Di Jin, Pengfei Jiao, Yuxiao Huang
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on real-world edges-missing networks show that our model can effectively detect community structures while completing missing edges. |
| Researcher Affiliation | Academia | 1College of Intelligence and Computing, Tianjin University, Tianjin, China 2Center of Biosafety Research and Strategy, Law School, Tianjin University, Tianjin, China 3Data Science, George Washington University, Washington, D.C., USA {hedongxiao, dedao, pjiao, jindi}@tju.edu.cn, yuxiaohuang@gwu.edu |
| Pseudocode | No | The paper describes the E-Step and M-Step of its inference method with equations, but it does not include any structured pseudocode or an explicitly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any statement about releasing source code or include links to a code repository. |
| Open Datasets | No | The paper lists the names of several real-world networks used for experiments (e.g., 'Zachary s karate club', 'Dolphin social network', 'Pubmed') but does not provide specific links, DOIs, or citations with author/year information for accessing these datasets directly. |
| Dataset Splits | No | The paper describes how edges-missing networks were produced for testing (e.g., 'randomly removed 20% existing edges'), but it does not specify a separate validation dataset split or a cross-validation strategy for model training/tuning. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used to run the experiments, such as CPU or GPU models, or cloud computing specifications. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., Python, specific libraries, or frameworks). |
| Experiment Setup | Yes | We use the default settings for all the baselines, and set α= 0.5 for our method (see the parameter analysis section later). (...) When α is in the range of 0.4 to 0.5, SGCD is often stable and gives better results and this trend is similar on other networks. Therefore, we set α = 0.5 without loss of generality. |