复杂网络社区发现算法分析-计算机应用技术专业论文.docxVIP

摘 摘 要 摘 要 如今人们生活在一个充满着各种各样的复杂网络的世界。网络社区结构是复 杂网络最普遍和最重要的拓扑属性之一，具有相同社区内节点相互连接紧密、不 同社区间节点相互连接稀疏的特点。揭示网络社区结构的复杂网络社区发现方法 对分析复杂网络拓扑结构、发现其隐含模式、预测其行为都具有十分重要的理论 意义，在社会网、生物网和万维网中具有广泛应用。 本文在对各种经典的复杂网络社区发现算法进行深入研究和分析的基础上， 针对复杂网络领域内对社区结构的定义不明晰的问题，对复杂网络的社区结构进 行重新定量定义，依据社区内节点的联系边数定来义社区是否紧密，并把社区内 联系边数与社区外联系边数之差的大小作为社区结构划分好坏的衡量标准。 根据已定义社区结构划分的衡量标准，提出了相应的新的网络社区发现算法。 该算法通过发现网络图中的超级密集核心区来得到网络社区的核心区，并以核心 区为中心，通过添加联系紧密的节点得到我们的社区结构，然后对社区结构进行 必要的合并和调整，以此得到最终的社区结构。该算法不仅不需要预先得到任何 关于网络社区个数的参数，也不需要人工设置算法的终止条件，还能有效地处理 各种规模不同的数据集。最后，通过实验验证分析了新算法的合理性和有效性。 关键词：复杂网络、社区发现、网络社区结构、网络聚类 Ab Abstract Abstract Nowadays, people have been living in a world filled with all sorts of complex networks. The community structure of the complex networks is one of the most common and important topological properties. In this structure, the same cluster nodes connect to each other closely, while different cluster nodes interconnect sparsely. The community discovery method which is expected to reveal the the community structure of complex network is of important theoretical significance in analysis of topology structure in complex network and is helpful in finding hidden patterns and predictions of its behavior. It has been widely applied in social networks, biological networks and the World Wide Web. In this paper, we do deep research and analysis on a variety of classic community discovery algorithms in complex networks. In order to address the limitations of the classical algorithms, a new definition and a new evaluation standard of the complex network community structure are given. Weather a community is close depends on the links of nodes in it. The quality of community structure is measured by the difference sizes of edges in communities and edges between communities. Based on the new definitions, a new community detecting algorithm is proposed. The algorithm gets an initial community by finding a super dense core area in the network, and then adds nodes to the community that is closely linked to it.finally the