IEEE Trans Cybern. 2022 Oct;52(10):10014-10026. doi: 10.1109/TCYB.2021.3071542. Epub 2022 Sep 19.
This article proposes a neighbors' similarity-based fuzzy community detection (FCD) method, which we call "NeSiFC." In the proposed NeSiFC approach, we compute the similarity between two neighbors by introducing a modified local random walk (mLRW). Basically, in a network, a node and its' neighbors with noticeable similarities among them construct a community. To measure this similarity, we introduce a new metric, called the peripheral similarity index (PSI). This PSI is used to construct the transition probability matrix for the mLRW. The mLRW is applied for each node until it meets a parameter called step coefficient. The mLRW gives better neighbors' similarity for community detection. Finally, a fuzzy membership function is used iteratively to compute the membership degrees for all nodes with reference to existing communities. The proposed NeSiFC has no dependence on the network characteristics, and no adjustment or fine tuning of more than one parameter is needed. To show the efficacy of the proposed NeSiFC approach, we provide a thorough comparative performance analysis considering a set of well-known FCD algorithms viz., the genetic algorithm for fuzzy community detection, membership degree propagation, center-based fuzzy graph clustering, FMM/H2, and FuzAg on a set of popular benchmarks, as well as real-world datasets. For both disjoint and overlapping community structures, results of various accuracy and quality metrics indicate the outstanding performance of our proposed NeSiFC approach. The asymptotic complexity of the proposed NeSiFC is found as O(n).
本文提出了一种基于邻居相似性的模糊社区检测（FCD）方法，我们称之为“NeSiFC”。在提出的 NeSiFC 方法中，我们通过引入改进的局部随机游走（mLRW）来计算两个邻居之间的相似性。基本上，在一个网络中，一个节点及其具有明显相似性的邻居构成一个社区。为了衡量这种相似性，我们引入了一个新的度量标准，称为外围相似性指数（PSI）。这个 PSI 用于构建 mLRW 的转移概率矩阵。mLRW 应用于每个节点，直到遇到一个称为步长系数的参数。mLRW 为社区检测提供了更好的邻居相似性。最后，使用模糊隶属度函数迭代计算所有节点的隶属度，参考现有社区。所提出的 NeSiFC 不依赖于网络特征，不需要调整或微调超过一个参数。为了展示所提出的 NeSiFC 方法的有效性，我们在一组流行的基准上以及真实世界的数据集中，针对一组著名的 FCD 算法（即模糊社区检测的遗传算法、隶属度传播、基于中心的模糊图聚类、FMM/H2 和 FuzAg）进行了彻底的对比性能分析。对于不相交和重叠的社区结构，各种准确性和质量指标的结果表明，我们提出的 NeSiFC 方法具有出色的性能。所提出的 NeSiFC 的渐近复杂度被发现为 O(n)。
