A Weighted Multi-Local-World Network Evolving Model and Its Application in Software Network Modeling
A Weighted Multi-Local-World Network Evolving Model and Its Application in Software Network Modeling
复制标题
加权多局部世界网络演化模型及其在软件网络建模中的应用
DOI：
10.1155/2018/2048525
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发表时间：
2018-05
影响因子：
--
通讯作者：
Bing Li
中科院分区：
文献类型：
--
作者：
Zengyang Li;Hui Liu;Jun-an Lu;Bing Li
本地世界（也称为社区）现象存在于许多现实生活网络中，例如计算机网络和社交网络。我们提出了加权多局部世界（WMLW）网络演化模型，考虑到局部世界中节点之间的密集链接、不同局部世界节点之间的稀疏链接以及局部世界内链接和局部世界间链接之间的不同重要性。在拓扑演化中，现有局部世界和新局部世界之间的新链接被添加到网络中，同时新节点和链接被添加到现有局部世界中。在权重机制上，局部世界内的链接权重和不同局部世界之间的链接权重被赋予了不同的含义。理论上证明WMLW模型生成的网络的强度分布服从幂律分布。仿真验证了理论结果的正确性。同时，度分布也遵循幂律分布。分析和仿真结果表明，所提出的WMLW模型可以用于对软件系统类图的演化进行建模。
The phenomenon of local worlds (also known as communities) exists in numerous real-life networks, for example, computer networks and social networks. We proposed the Weighted Multi-Local-World (WMLW) network evolving model, taking into account the dense links between nodes in a local world, the sparse links between nodes from different local worlds, and the different importance between intra-local-world links and inter-local-world links. On topology evolving, new links between existing local worlds and new local worlds are added to the network, while new nodes and links are added to existing local worlds. On weighting mechanism, weight of links in a local world and weight of links between different local worlds are endowed different meanings. It is theoretically proven that the strength distribution of the generated network by the WMLW model yields to a power-law distribution. Simulations show the correctness of the theoretical results. Meanwhile, the degree distribution also follows a power-law distribution. Analysis and simulation results show that the proposed WMLW model can be used to model the evolution of class diagrams of software systems.
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影响因子：
4.1
作者：
Decan, Alexandre;Mens, Tom;Grosjean, Philippe
通讯作者：
Grosjean, Philippe
DOI：
10.1142/s0218194017500401
发表时间：
2017-09
期刊：
Int. J. Softw. Eng. Knowl. Eng.
影响因子：
--
作者：
G. Concas;M. Marchesi;C. Monni;Matteo Orrù;R. Tonelli
通讯作者：
G. Concas;M. Marchesi;C. Monni;Matteo Orrù;R. Tonelli
DOI：
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发表时间：
2003-07
期刊：
arXiv: Disordered Systems and Neural Networks
影响因子：
--
作者：
S. Valverde;R. Solé
通讯作者：
S. Valverde;R. Solé
影响因子：
1.7
作者：
Li, Ping;Zhao, Qingzhen;Wang, Haitang
通讯作者：
Wang, Haitang
DOI：
10.1016/j.jss.2017.08.017
发表时间：
2017-11
期刊：
J. Syst. Softw.
影响因子：
--
作者：
Chun Yong Chong;S. Lee
通讯作者：
Chun Yong Chong;S. Lee