基于网络节点中心性的新闻重要性评价研究

评价权威报刊的新闻重要性对于正确理解国家政策变化具有重要意义。该文以《人民日报》为例，抽取发表在1946?2008年期间的新闻，利用其内容相似性构建新闻网络。从复杂网络视角，一篇新闻与其他新闻的相似性越高，其在新闻网络中连接越紧密，具有较大的节点中心性。鉴于此，该文将H指数引入PageRank排序算法，提出H-PageRank排序算法，利用其计算H-PageRank中心性，评价新闻重要性。在实验过程中，考虑到不同领导核心执政时期《人民日报》的新闻风格与新闻版面的差异性将新闻划分为4个时代，基于表示学习分别形成对应的新闻网络。研究结果表明:1) 4个新闻网络的拓扑结构都表现出高聚类性与同配性，且具有近似幂律的度分布，表现出复杂网络一般特性；2) 基于多种网络节点中心性指标，对每个新闻网络中的节点进行全局排序，并以是否成为头版新闻为重要性的评价准则计算得到相近的AUC值，然后基于局部排序的Top-N评价方法计算得到正确率、召回率和F1指标，综合以上指标的实验结果表明，H-PageRank中心性显著优于其他算法的中心性，验证H-PageRank排序算法的有效性；3) 针对每个新闻网络，基于网络节点中心性的Top-N评价方法不同排序列表长度条件，其计算得到的正确率显著高于理论基准，表明评价方法的鲁棒性。

Research on Importance Evaluation of News Based on Nodal Centralities of Complex Network

It is of great significance to correctly evaluate the importance of news in national newspapers and magazines for better understanding the changes of national policies. In this paper, we take People’s Daily as an example, extract news published in 1946?2008, and construct news network by using their content-based similarities. In the view of complex network, news has higher similarities with others, making it be closely connected and larger nodal centrality in news network. In respect to this, we propose an H-PageRank ranking algorithm by introducing the H-index to improve the PageRank ranking algorithm. In the experiment, all news in People’s Daily is divided into four stages according to their styles and editions in different governing times, which is respectively used to construct news networks based on representation learning. The experimental results show that 1) the topologies of four news networks all have a general properties of complex network, including the high clustering coefficients, positive assortativity coefficients and approximately power-law degree distributions; 2) each news network presnets a mostly similar AUC calculated by the global rank score of the front-page news according to diverse nodal centralities, however the precision, recall and F1-score calculated by the Top-N evaluating model according to the H-PageRank centrality are optimal, which validate the efficiency of local ranking news according to the H-PageRank centrality; 3) the precision of each news network is significantly superior to the theoretical baselines even when the ranking list is restricted into different length, which suggests the roubustness of evaluating model.