Community structure is an integral characteristic of real world networks whichever processes or areas they emerge from. This paper addresses the problem of community structure detection theoretically as well as computationally. The authors introduce a number of concepts such as the neighbourhood and strength of a subgraph, p-community, local maximal p-community, hubs, and outliers that play elemental role in formalising the concept of community structure in complex networks. A few preliminary results have been derived that lead to the development of an algorithm for community structure detection in undirected unweighted networks. The algorithm is based on a local seed expansion strategy that uses the concept of interaction coefficient. The authors have analysed the algorithm on a number of parameters such as accuracy, stability, and quality on synthetic and real world networks from different areas.

社区结构是现实世界网络的一个整体特征，无论它们来自哪个过程或领域。本文从理论上和计算上都解决了社区结构检测的问题。作者介绍了许多概念，例如，子图的邻域和强度，p社区，局部最大p社区，中心和离群值，它们在形式化复杂网络中的社区结构概念中起着基本作用。已经获得了一些初步结果，这些结果导致开发了用于无向非加权网络中的社区结构检测算法。该算法基于使用交互系数概念的局部种子扩展策略。作者已经从许多参数（例如准确性，稳定性，