The identifying of the most influential nodes in the complex network is of great significance for information dissemination and control. We collect actual data from Sina Weibo and establish two user relationship networks based on bi-directional”concern”. By analyzing the statistical characteristics of the network topology, we find that each of them has a small world and scale free character-istics. Moreover, we describe four network centrality indicators, including node degree, closeness, betweenness and k-Core. Through empirical analysis of four-centrality metric distribution, we find that the node degrees follow a segmented power-law distribution;be-tweenness difference is most significant;both networks possess significant hierarchy, but not all of the nodes with higher degree have the greater k-Core values;strong correlation exists between the centrality indicators of all nodes, but this correlation is weakened in the node with higher degree value. The two networks are used to simulate the information spreading process with the SIR information dissemination model based on infectious disease dynamics. The simulation results show that there are different effects on the scope and speed of information dissemination under different initial selected individuals. We find that the closeness and k-Core can be more accurate representations of the core of the network location than other indicators, which helps us to identify influential nodes in the information dissemination network.% 根据新浪微博的实际数据，建立了两个基于双向“关注”的用户关系网络，通过分析网络拓扑统计特征，发现二者均具有小世界、无标度特征。通过对节点度、紧密度、介数和k-core四个网络中心性指标进行实证分析，发现节点度服从分段幂率分布；介数相比其他中心性指标差异性最为显著；两个网络均具有明显的层次性，但不是所有度值大的节点核数也大；全局范围内各中心性指标之间存在着较强的相关性，但在度值较大的节点群这种相关性明显减弱。此外，借助基于传染病动力学的SIR信息传播模型来分析四种指标在刻画节点传播能力方面的差异性，仿真结果表明，选择具有不同中心性指标的初始传播节点，对信息传播速度和范围均具有不同影响；紧密度和k-core较其他指标可以更加准确地描述节点在信息传播中所处的网络核心位置，这有助于识别信息传播拓扑网络中的关键节点。
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