With the development of economy and society, network analysis is widely used in more and more fields. Signed network has a good effect in the process of representation and display. As an important part of network analysis, fuzzy community detection plays an increasingly important role in analyzing and visualizing the real world. Fuzzy community detection helps to detect nodes that belong to some communities but are still closely related to other communities. These nodes are helpful for mining information from the network more realistically. However, there is little research in this field. This paper proposes a fuzzy community detection algorithm based on pointer and adjacency list. The model adopts a new ICALF network data structure, which can achieve the effect of storing community partition structure and membership value between community and node at the same time, with low time complexity and storage space. Experiments on real networks verify the correctness of the method, and prove that the method is suitable for large-scale network applications.

中文翻译：

---

一种基于改进社区邻接表的模糊社区检测方法

随着经济和社会的发展，网络分析被广泛应用于越来越多的领域。签名网络在表示和展示过程中具有良好的效果。作为网络分析的重要组成部分，模糊社区检测在现实世界的分析和可视化中发挥着越来越重要的作用。模糊社区检测有助于检测属于某些社区但仍与其他社区密切相关的节点。这些节点有助于更真实地从网络中挖掘信息。然而，这方面的研究很少。提出一种基于指针和邻接表的模糊社区检测算法。该模型采用全新的 ICALF 网络数据结构，可以达到同时存储社区和节点之间的社区划分结构和成员价值的效果，时间复杂度低，存储空间小。真实网络实验验证了该方法的正确性，证明该方法适用于大规模网络应用。