摘要
In social networks, link prediction is the task to identify links in future. Many existing link prediction techniques used similarity scores to predict links. An essential concern in the link prediction problem is identifying missing links between the nodes when there are no common neighbors between the nodes. Considering this, a new algorithm proposed, namely Similarity-based Algorithm using Degree and Common Neighbour (SADCN) which includes a node's degree in the shortest path and common neighbor. For experiment evaluation, three datasets are used to test our method performance against some standard similarity index and the recently proposed algorithms for link prediction, which depicts that our approach achieved comparable AUC values to those that consider common neighbors and it gives better AUC for those links, where no mutual neighbour between the two nodes exists. Finally, we create feature vectors and use XGB classifiers for predicting links. It shows that our proposed algorithm can improve the F-measure and accuracy in a feature based link prediction model.
摘要译文
在社交网络中，链接预测是确定未来链接的任务。许多现有的链接预测技术都使用相似性得分来预测链接。链接预测问题中的一个基本问题是，当节点之间没有共同的邻居时，识别节点之间的缺失链接。考虑到这一点，是一种新的算法，即使用学位和共同邻居（SADCN）的基于相似性的算法，其中包括最短路径和共同邻居的节点学位。为了进行实验评估，使用三个数据集来测试我们的方法性能针对某些标准相似性指数和最近提出的链接预测算法，这描述了我们的方法与考虑常见邻居的人的AUC值相当，并且为这些链接提供了更好的AUC ，两个节点之间没有相互邻居的地方。最后，我们创建功能向量并使用XGB分类器来预测链接。它表明我们提出的算法可以提高基于功能的链接预测模型中的F量和准确性。
Singh; Ankita[1];Singh; Nanhay[2]. An approach for predicting missing links in social network using node attribute and path information[J]. International Journal of System Assurance Engineering and Management, 2022,13(2): 944-956