Community detection with dependent connectivity
连接存在依赖时的社区检测AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究网络连接依赖、社区检测及国家间农产品贸易网络。
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连接存在依赖时的社区检测
- 研究问题
- 网络社区内部的边存在相关性、不同社区之间的边又具有异质性时，如何开展社区检测并改善独立边模型的估计表现？
- 方法与思路
- 作者通过 Bahadur 表示纳入社区内连接依赖，无需指定完整似然；随后分析收敛与偏差，并用模拟和贸易、脑成像网络展示。
- 材料陈述的贡献
- 摘要称，利用相关信息可比独立随机块模型获得更快收敛，所提算法相对变分 EM 降低偏差并加快收敛，模拟也显示改善。
- 阅读关注点
- 应核对连接依赖的允许范围、异质性设定和社区可识别条件，并查看收敛比较的基准、算法实现及模拟是否公平匹配。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；优势成立的条件和数值结果未提供，不能推断任意依赖网络都获益，也不等于识别网络干扰效应。
原文摘要
In network analysis, within-community members are more likely to be connected than between-community members, which is reflected in that the edges within a community are intercorrelated. However, existing probabilistic models for community detection such as the stochastic block model (SBM) are not designed to capture the dependence among edges. In this paper, we propose a new community detection approach to incorporate intra-community dependence of connectivities through the Bahadur representation. The proposed method does not require specifying the likelihood function, which could be intractable for correlated binary connectivities. In addition, the proposed method allows for heterogeneity among edges between different communities. In theory, we show that incorporating correlation information can achieve a faster convergence rate compared to the independent SBM, and the proposed algorithm has a lower estimation bias and accelerated convergence compared to the variational EM. Our simulation studies show that the proposed algorithm outperforms the existing multinetwork community detection methods assuming conditional independence among edges. We also demonstrate the application of the proposed method to agricultural product trading networks from different countries and to brain fMRI imaging networks.