In the "We the Media" era, the rules for forming social users' following relationships are complex. Links generated between two social user nodes are influenced by not only the structural information of their social network nodes but also the users’ occupational environments, interests in opinions, topics, social psychology, etc. In the existing studies of link prediction in complex networks, predicting the possibility of link generation between two nodes that have not yet generated edges in a complex network is calculated mainly from the known network nodes and structural information. Such studies, in which the main predictors are the structural similarities among social nodes or user location nodes, cannot fully explore and utilize the social network node users’ public opinion characteristics. To quantitatively identify the influence of different dimensions of public opinion factors on predicting links between social user nodes, we present a study on the prediction of social network links in "We the Media" networks. Starting with the characteristics of the elements found in public opinions on “We the Media” networks, in which public opinions are multidimensional and multilayered and possess multiple attributes, we built a multidimensional network model oriented towards the topology of public opinions on “We the Media” networks. Combined with an analysis of the driving factors in the formation of user-node relationships in social networks, we designed a prediction algorithm that works on multidimensional network links. Furthermore, we conducted an empirical analysis of social relationship prediction, whose effectiveness has also been compared with baseline methods such as the Common-Neighbourhood-Driven model, the Jaccard index, and the SimRank method. We chose the area under the curve (AUC) as the indicator of link prediction and evaluation using “We the Media” public opinion data from Weibo.com. The research findings of this paper can be summarized as follows: (1) The effectiveness of the multidimensional network link prediction algorithm is significantly higher than those of the baseline methods. (2) The prediction algorithm presented in this paper works on multidimensional network links and can evaluate the effects of different dimensions of public opinion factors on the prediction of user-node links in social networks. (3) The element of occupational environment improves the accuracy much more than the element of user interest in opinions and topics when predicting user-nodes’ links, while the element of social psychology reduces the accuracy.

在“我们的媒体”时代，形成社交用户的追随关系的规则很复杂。两个社交用户节点之间生成的链接不仅受其社交网络节点的结构信息的影响，而且还受到用户的职业环境，观点，主题，社会心理的兴趣等的影响。在现有的复杂网络链接预测研究中，主要根据已知的网络节点和结构信息来预测在复杂网络中尚未生成边缘的两个节点之间的链接生成可能性的预测。这样的研究主要预测因素是社交节点或用户位置节点之间的结构相似性，但这些研究无法充分探索和利用社交网络节点用户的公众舆论特征。为了定量确定不同维度的舆论因素对预测社交用户节点之间链接的影响，我们对“我们媒体”网络中社交网络链接的预测进行了研究。从“我们媒体”网络中舆论元素的特征入手，其中，舆论是多维的，多层的，具有多重属性的，我们建立了一个面向“我们的媒体”舆论拓扑结构的多维网络模型。 ”网络。结合对社交网络中用户-节点关系形成的驱动因素的分析，我们设计了一种适用于多维网络链接的预测算法。此外，我们对社会关系预测进行了实证分析，其有效性也已与基准方法（例如，“社区驱动模型”，“ Jaccard索引”和“ SimRank”方法）进行了比较。我们使用来自微博的“ We the Media”舆论数据，选择曲线下面积（AUC）作为链接预测和评估的指标。本文的研究成果可以概括如下：（1）多维网络链路预测算法的有效性明显高于基线方法。（2）本文提出的预测算法适用于多维网络链接，并且可以评估不同维度的舆论因素对社交网络中用户节点链接的预测的影响。