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近年来,随着网络信息资源的不断增加,其中包括因特网、万维网以及社会和生物化学网络、复杂网络的规模不断加大,作为研究复杂网络的一种新的方法,社团检测的工作量也不断增加。为解决这一问题,人们进行了大量的试验研究,提出了许多新的社团算法。然而有的算法虽然效果较好,但计算量太大,例如GN算法。本文所述算法是基于GN算法,并对其进行了改进的算法。这个算法是Newman提出的一个基于模块化的快速算法,在减少计算量的同时与GN算法有相似的效果。快速算法是对GN算法的发展,又是在后续社团研究中的基础,所以对这一算法比较深入的研究,对后续社团算法甚至复杂网络的研究都有着重要的意义。通过对这一算法在蛋白质交互网中的应用,可以进一步理解并研究网络社团算法。
In recent years, with the continuous increase of network information resources, including the Internet, the World Wide Web and social and biochemical networks, the scale of complex networks has been increasing. As a new method of studying complex networks, the workload of community testing also continues increase. To solve this problem, a large number of experimental studies have been conducted and many new community algorithms have been proposed. However, some algorithms work well, but the computations are too large, such as the GN algorithm. The algorithm described in this article is based on the GN algorithm, and its improved algorithm. This algorithm is a fast, modularization-based algorithm proposed by Newman that has the same effect as the GN algorithm while reducing the amount of computation. Fast algorithm is the development of GN algorithm and is the basis of subsequent community research. Therefore, the more in-depth study of this algorithm is of great significance to the research of subsequent community algorithms and even complex networks. Through the application of this algorithm in the protein interactive network, we can further understand and study the network association algorithm.