Bayesian Approach to Modeling and Detecting Communities in Signed Network
Authors: Bo Yang, Xuehua Zhao, Xueyan Liu
AAAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through the comparisons with state-of-the-art methods on synthetic and real-world networks, the proposed approach shows its superiority in both community detection and sign prediction for exploratory networks. |
| Researcher Affiliation | Academia | Bo Yang, Xuehua Zhao, and Xueyan Liu School of Computer Science and Technology, Jilin University, Changchun, China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, China ybo@jlu.edu.cn |
| Pseudocode | Yes | Table 1: SSL Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | No | The paper mentions synthetic and real-world networks (Slovene parliamentary party network (Kropivnik and Mrvar 1996), Gahuku Gama subtribes network (Read 1954), monastery network (Doreian and Mrvar 1996)), but does not provide concrete access information (link, DOI, repository, or specific citation that *enables dataset access*) for them to be considered publicly available in a reproducible manner. For example, the citations for the real-world networks are for papers, not direct dataset links. |
| Dataset Splits | No | The paper describes how synthetic networks are generated with parameters like SG(c, n, k, pin, p−, p+), and mentions using |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper describes the generation of synthetic networks with parameters like c, n, k, pin, p-, p+, and discusses sampling rates and noise rates (s, ε) for experiments. However, it does not explicitly provide hyperparameter values for the proposed SSBM or system-level training settings in a dedicated 'Experimental Setup' section or similar. |