Community Detection via Local Learning Based on Generalized Metric With Neighboring Regularization
Community Detection via Local Learning Based on Generalized Metric With Neighboring Regularization
复制标题
基于具有邻近正则化的广义度量的本地学习社区检测
DOI：
10.1109/tsmc.2020.3003019
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发表时间：
2022-01
期刊：
影响因子：
--
通讯作者：
Xinzhe Qi
中科院分区：
文献类型：
--
作者：
Guangliang Gao;Zhiang Wu;Lu Zhang;Jie Cao;Xinzhe Qi
社区检测一直是网络分析中的一个基本问题。之前的大量研究都将社区检测视为一个优化过程，其中各种内部质量指标通常被视为目标函数，例如模块化（${Q}$）和加权社区聚类（WCC）。然而，纯粹优化预定义的质量度量可能导致检测到的社区的规模的极端不平衡，例如，一些巨大的社区和许多非常小的社区为了在合适的社区数量下揭示大网络内部真实的介观结构，我们提出了一种新的社区检测框架LL-GMR，这是一种基于广义度量和邻域正则化的局部学习框架。LL-GMR适用于非重叠和重叠检测任务。在LL-GMR中，我们提出了一个广义的表示，并说明它可以被实例化为12个著名的内部质量度量。当广义度量被用作目标函数时，我们将节点级和社区级邻域信息编码为两个正则化项，以缓解不平衡社区的困境。实验结果表明，我们的LL-GMR在发现六个真实网络中的真实社区方面始终优于其他最先进的社区检测方法。
Community detection has long been a fundamental problem in network analysis. A great deal of previous research has regarded community detection as an optimization process, where a variety of internal quality metrics are typically treated as objective functions, such as modularity ( ${Q}$ ) and weighted community clustering (WCC). However, purely optimizing a predefined quality metric probably results in an extreme unbalance in the scale of the detected communities, e.g., few giant communities with many very small communities. To reveal the true mesoscopic structure inside a big network under the suitable number of communities, we propose a novel community detection framework called LL-GMR, which is a local learning framework based on generalized metric with neighboring regularization. LL-GMR is qualified for both nonoverlapping and overlapping detection tasks. In LL-GMR, we propose a generalized representation and illustrate that it can be instantiated into 12 well-known internal quality metrics. When the generalized metric is used as an objective function, we encode node-level and community-level neighborhood information into two regularization terms to alleviate the dilemma of unbalanced communities. The experimental results show that our LL-GMR consistently outperforms other state-of-the-art community detection approaches in terms of discovering ground-truth communities in six real-life networks.
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影响因子：
10.5
作者：
M. Rosvall;Carl T. Bergstrom
通讯作者：
M. Rosvall;Carl T. Bergstrom
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影响因子：
10.6
作者：
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通讯作者：
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DOI：
10.1016/j.patrec.2015.11.008
发表时间：
2016-01
期刊：
Pattern Recognition Letters(PRL)
影响因子：
--
作者：
Hengyuan Zhang;Xiaowu Chen;Jia Li;Bin Zhou
通讯作者：
Bin Zhou
影响因子：
1.1
作者：
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通讯作者：
Liangli Zhen;Zhang Yi;Xi Peng;Dezhong Peng
DOI：
10.1145/3110025.3110125
发表时间：
2017-07
期刊：
2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)
影响因子：
--
作者：
Eduar Castrillo;Elizabeth León Guzman;Jonatan Gómez
通讯作者：
Eduar Castrillo;Elizabeth León Guzman;Jonatan Gómez