Most real-world networks suffer from incompleteness or incorrectness, which is an inherent attribute to real-world datasets. As a consequence, those downstream machine learning tasks in complex network like community detection methods may yield less satisfactory results, i.e., a proper preprocessing measure is required here. To address this issue, in this paper, we design a new community attribute based link prediction strategy HAP and propose a two-step community enhancement algorithm with automatic evolution process based on HAP. This paper aims at providing a community enhancement measure through adding links to clarify ambiguous community structures. The HAP method takes the neighbourhood uncertainty and Shannon entropy to identify boundary nodes, and establishes links by considering the nodes' community attributes and community size at the same time. The experimental results on twelve real-world datasets with ground truth community indicate that the proposed link prediction method outperforms other baseline methods and the enhancement of community follows the expected evolution process.
翻译:大多数实际世界网络都存在不完整或不正确的情况,这是真实世界数据集的固有属性,因此,在复杂的网络中,诸如社区探测方法等复杂网络中的下游机器学习任务可能会产生不那么令人满意的结果,即需要适当的预处理措施。为了解决这个问题,我们在本文件中设计了一个新的社区属性链接预测战略HAP,并提出了一个基于HAP的基于自动演化过程的两步社区增强算法。本文件的目的是通过增加联系,澄清模糊的社区结构,提供社区增强能力的措施。HAP方法采用邻里不确定性和香农通心机识别边界节点,并通过同时考虑节点社区属性和社区规模来建立联系。12个与地面真相社区一起建立的12个实际世界数据集的实验结果表明,拟议的联系预测方法优于其他基线方法,加强社区则遵循预期的演进过程。