| 基于Web 2.0的边与节点同时增长网络模型 |
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| 引用本文： | 熊菲,刘云,司夏萌,丁飞.基于Web 2.0的边与节点同时增长网络模型[J].物理学报,2010,59(10):6889-6895. |
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| 作者姓名： | 熊菲 刘云 司夏萌 丁飞 |
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| 作者单位： | 北京交通大学通信与信息系统北京市重点实验室,北京,100044 |
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| 基金项目： | 国家自然科学基金(批准号:60972012)、北京市自然科学基金(批准号:4102047)、教育部哲学人文社会科学研究重大课题(批准号:08WL1101)、北京市教育委员会学科建设与研究生建设项目(批准号:JXKJD20090001)和科技人员服务企业计划(2009GJA00048)资助的课题. |
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| 摘 要： | 模拟了Web2.0网络的发展过程并研究其拓扑结构,分析某门户网站实际博客数据的度分布、节点度时间变化,发现与先前的无标度网络模型有所差别.根据真实网络的生长特点,提出了边与节点同时增长的网络模型,包括随机连接及近邻互联的网络构造规则.仿真研究表明,模拟的网络更接近实际,在没有优先连接过程时,模型能得到幂率的度分布;并且网络有更大的聚类系数以及正的度相关性。
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| 关 键 词： | 复杂网络 Web 2.0网络 博客 幂率分布 |
| 收稿时间： | 2009-11-20 |
| 修稿时间： | 2/3/2010 12:00:00 AM |
Network model with synchronously increasing nodes and edges based on Web 2.0 |
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| Xiong Fei,Liu Yun,Si Xia-Meng,Ding Fei.Network model with synchronously increasing nodes and edges based on Web 2.0[J].Acta Physica Sinica,2010,59(10):6889-6895. |
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| Authors: | Xiong Fei Liu Yun Si Xia-Meng Ding Fei |
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| Institution: | Key Laboratory of Communication and Information Systems-Beijing Jiaotong University, Beijing Municipal Commission of Education, Beijing 100044, China;Key Laboratory of Communication and Information Systems-Beijing Jiaotong University, Beijing Municipal Commission of Education, Beijing 100044, China;Key Laboratory of Communication and Information Systems-Beijing Jiaotong University, Beijing Municipal Commission of Education, Beijing 100044, China;Key Laboratory of Communication and Information Systems-Beijing Jiaotong University, Beijing Municipal Commission of Education, Beijing 100044, China |
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| Abstract: | We investigate the growing process and topological features of Web 2.0 networks. By analyzing the network’s degree distribution, average degree and time evolution of the node degree of an actual blog on portal website, we found these properties are different from those of the former scale-free network models. According to the growth characteristics of actual networks, we put forward a new type of network with synchronously increasing nodes and edges, including construction algorithms of randomly linking and connection between close neighbours. The simulation results show that the networks generated from our model have power-law degree distribution in case of absence of the preferential attachment process, and the clustering coefficient increases and the connectivity correlations are assortative. |
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| Keywords: | complex networks Web 2 0 network blog power-law distribution |
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