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.
Community Focusing: Yet Another Query-Dependent Community Detection
Authors: Zhuo Wang, Weiping Wang, Chaokun Wang, Xiaoyan Gu, Bo Li, Dan Meng329-337
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 20,106 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,130 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 | Extensive experimental results on real and synthetic networks demonstrate the performance of our methods. |
| Researcher Affiliation | Academia | Zhuo Wang,1,3 Weiping Wang,1 Chaokun Wang,2 Xiaoyan Gu,1 Bo Li,1 Dan Meng1 1Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China 2School of Software, Tsinghua University, Beijing, China 3School of Cyber Security, University of Chinese Academy of Sciences, Beijing, China |
| Pseudocode | No | The paper describes methods like OPT-ma and OPT-cd using numbered steps within the text, but these are not formatted as distinct pseudocode blocks or explicitly labeled 'Algorithm' figures. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Real-world networks are summarized in Table 2. They are publicly available from Stanford Network Analysis Project (snap.stanford.edu), and provide ground-truth communities. Synthetic networks are generated using the LFR benchmark (Lancichinetti, Fortunato, and Radicchi 2008). |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, and test dataset splits for its experiments. It evaluates on publicly available and synthetic networks with ground-truth communities without specifying such splits. |
| Hardware Specification | Yes | The experiments are conducted on a Linux Server with 128GB main memory and Intel Xeon CPU E5-2630 (2.4GHz). |
| Software Dependencies | No | The paper states 'The methods are written in C++' but does not provide specific version numbers for compilers, libraries, or other software dependencies. |
| Experiment Setup | Yes | In FLCF, η is a size constraint of a resulted subgraph, and α is the tuning factor of the combinational density. Through empirical evaluation, η is set to 200. For each network, α is set to the number achieving the highest F1 (α is set to 0.0, 0.8, 1.0, 0.4, and 0.2 for Amazon, DBLP, Youtube, Live Journal, and Orkut respectively). |