基于复杂网络的大型互联网企业高管分析

采用复杂网络分析特定属性群体。以互联网企业高管简历作为原始数据,以高管姓名和分词系统抽取实体关键词作为节点,个人简历中是否包含关键词作为连接边的条件进行建模,使用复杂网络理论对所建网络进行分析。实验结果表明,部分关键词节点度值存在明显差异,归一化后的特征向量明显大于介数。通过统计分析发现,美国和北京相关背景很重要,同时对比归一化后的介数和特征向量证明两点:第一,社会关系中个体涉及的实体对象比在社会关系网络中的位置更为重要;第二,跨行业跳槽人员的职业背景经历可能会给其在新的企业中的个人发展带来不利影响。

Analysis of Internet Enterprises' Top Executives Based on Complex Networks

Network theory can be used to analyze specific attribute groups. Using the executives'' resumes of top Internet enterprises as data source, defining executives'' name and entity keywords extracted by a word segmentation system as nodes and constructing links between the executive and keyword, when the keyword is in his resume, a network was built. Based on the network theory and the presented model, complex network characteristics were explored and analyzed. The experiment results show that the degree values of some keywords are significantly different, meanwhile normalized eigenvectors are observably larger than the normalized betweeness centrality. The statistical analysis prove that the United States and Beijing related backgrounds are extremely important for executives of top Internet enterprises, and the comparison between the normalized betweenness and eigenvector demonstrates that entities that individuals connect to in social experience are more important than the position of those individuals in social networks, the career experience of cross-industry job-hopping may not conducive to individuals'' developments in a new enterprise.