摘要：The instant information exchanging network of the Internet is interpreted as the consequences of invisible connection between humans. In the graph based studies the nodes are human beings and the edges represent various social relationships. The interactions among users can be interpreted via the formation and evolution of semantics. The interactive as well as intertwined behaviors are the foundation of network itself; at the same time, they shape the way how and where the network will evolve. This paper proposes a network growth model based on the semantic similarity as well as popularity of nodes. In our model, the nodes represent Sina Weibo blogs and are with semantics, the links are subscribing hyperlinks between nodes. The probability of link establishment between two nodes then calculated from the similarity between nodes. The data and experiments are based on Sina Weibo blogs, which are the continuous results of interactions by users. We collect data using WebCrawler from Sina API, obtaining a portion of the whole network. Results show that the statistic properties of Sina Weibo are in close analogy with that of social network and also the characteristic complex network. The studied network contains a number of very high-degree nodes; these nodes are the cores which small groups strongly clustered, and low-degree nodes at the fringes of the network. However, some nodes with too much semantics (especially under one category) are in decreased chances of having links from newly added nodes. The reason may lies in that the over-abundant semantics remains confusion for knowledge acquiring.
