信息融合相似性算法下的虚拟社区链接预测

针对传统的基于节点相似性的链接预测方法存在链接预测指标仅考虑网络结构信息或者节点属性信息，以及链接预测指标静态处理节点之间关系的问题，提出了一种基于信息融合相似性算法的链接预测指标（similarity based on network evolution and user generated content ， SNEUGC），该指标结合用户生成内容信息和网络演化信息对含权网络进行链接预测，以解决现有链接预测指标在含权网络环境下链接预测准确率低的问题。实验证明，该方法的准确率达到了80％，具有一定的可行性。

Virtual Community Link Prediction Based on Similarity Algorithm with Fusion Information

The traditional link prediction methods based on the similarity of nodes have several problems .Only the structure of network or the information of nodes were considered as link prediction indexes .In the prediction indexes the relationship between two nodes was treated statically .A new link prediction index called SNEUGC on the basis of information fusion similarity algorithm was then proposed .The SNEUGC index makes link predictions in weighted networks considering both user generated content information and network evolution information , and can solve the problem of low prediction accuracy of those existed indexes in weighted networks .The result verifies that the accuracy of SNEUGC is up to 80%and the method is feasible .