Detect Overlapping Communities via Ranking Node Popularities
Authors: Di Jin, Hongcui Wang, Jianwu Dang, Dongxiao He, Weixiong Zhang
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To assess the performance of the proposed method, we evaluated it on real-world networks and synthetic benchmarks. We compared it with five state-of-the-art methods for overlapping community detection |
| Researcher Affiliation | Academia | 1School of Computer Science and Technology, Tianjin University, Tianjin 300072, China, 2School of Information Science, Japan Advanced Institute of Science and Technology, Japan, 3College of Math and Computer Science, Institute for Systems Biology, Jianghan University, Wuhan 430056, China, 4Department of Computer Science and Engineering, Washington University, St. Louis, MO 63130, USA |
| Pseudocode | No | The paper describes algorithmic steps but does not include structured pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that its source code is open or publicly available. |
| Open Datasets | Yes | Fortunately, six real networks and their overlapping communities have been published by the Stanford Network Analysis Project (Leskovec 2015)... A class of well-known synthetic benchmarks with overlapping community structures has been proposed by (Lancichinetti and Fortunato 2009), which is referred to as LFR hereafter. |
| Dataset Splits | No | The paper does not explicitly provide details about training/validation/test dataset splits. It describes generating subnetworks for testing but not formal splits of a single dataset. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers used for its implementation or experiments. |
| Experiment Setup | Yes | Our method has an additional parameter H which should be specified as a small positive value... In the subsequent experiments H was set to 1.0e-3. For these four sets of benchmarks above, the parameter H of the new method was set to 16e-3, 14e-3, 9e-3 and 9e-3, respectively. |