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用于符号网络分析的随机块建模和变分贝叶斯学习 |IEEE 期刊和杂志 |IEEE Xplore
Source: ieeexplore.ieee.org
Article summary:
1. Signed networks with positive and negative links contain more information than unsigned networks, making them attractive for study.
2. This article proposes a Bayesian method that uses a random block model to represent the community structure in signed network contexts, and a variational Bayesian EM algorithm for parameter estimation and evidence-based model selection approximation.
3. The proposed method is compared to recent methods on synthetic and real-world networks, showing its advantages in exploring network communities and symbol prediction.
Article analysis:
The article is generally trustworthy and reliable, as it provides an overview of the current state of research into signed networks with positive and negative links, as well as proposing a new Bayesian method for their analysis. The article is well-structured, clearly outlining the background of the research before introducing the proposed method. It also provides evidence for its claims by comparing the proposed method to existing methods on both synthetic and real-world networks.
The only potential bias in the article is that it does not explore any counterarguments or alternative approaches to signed network analysis beyond those mentioned in the introduction. However, this does not significantly detract from its trustworthiness or reliability since it focuses primarily on presenting its own approach rather than providing an exhaustive overview of all possible approaches to signed network analysis.