Hybrid Mixed-Membership Blockmodel for Inference on Realistic Network Interactions
Hybrid Mixed-Membership Blockmodel for Inference on Realistic Network Interactions
复制标题
DOI：
10.1109/tnse.2018.2823324
复制
发表时间：
2019-07-01
影响因子：
6.6
通讯作者：
Airoldi, Edoardo M.
中科院分区：
文献类型：
--
作者：
Kao, Edward K.;Smith, Steven Thomas;Airoldi, Edoardo M.
这项工作提出了一种新的混合混合成员块模型（HMMB），它集成了三个典型的网络模型，以捕捉现实世界的相互作用的特点：社区结构与混合成员，幂律分布的节点度和稀疏性。这种混合模型提供了现实所需的能力，使控制和推理的个人属性的利益，如混合成员和流行。一个严格的推理过程是通过迭代贝叶斯更新估计该模型的参数，有针对性的初始化，以提高可识别性。对于混合隶属度参数的估计，Cramer-Rao界是通过量化Fisher信息矩阵的信息含量而得到的。在模拟中的估计实现协方差接近Cramer-Rao界，同时保持良好的真理覆盖证明了所提出的推理的有效性。我们说明了实用的建议模型和推理过程中的应用程序中检测一个社区从几个线索节点，成功取决于准确估计的混合成员。对模拟和真实数据的性能评估表明，HMMB推理能够在存在挑战性社区重叠的情况下恢复混合成员资格，从而显着提高基于网络模块化和更简单模型的算法的检测性能。
This work proposes a novel hybrid mixed-membership blockmodel (HMMB) that integrates three canonical network models to capture the characteristics of real-world interactions: community structure with mixed-membership, power-law-distributed node degrees, and sparsity. This hybrid model provides the capacity needed for realism, enabling control and inference on individual attributes of interest such as mixed-membership and popularity. A rigorous inference procedure is developed for estimating the parameters of this model through iterative Bayesian updates, with targeted initialization to improve identifiability. For the estimation of mixed-membership parameters, the Cramer-Rao bound is derived by quantifying the information content in terms of the Fisher information matrix. The effectiveness of the proposed inference is demonstrated in simulations where the estimates achieve covariances close to the Cramer-Rao bound while maintaining good truth coverage. We illustrate the utility of the proposed model and inference procedure in the application of detecting a community from a few cue nodes, where success depends on accurately estimating the mixed-memberships. Performance evaluations on both simulated and real-world data show that inference with HMMB is able to recover mixed-memberships in the presence of challenging community overlap, leading to significantly improved detection performance over algorithms based on network modularity and simpler models.