Link prediction tremendously gained concern in the field of machine learning by virtue of its real-world applicability on various fields including social network analysis, biomedicine, e-commerce, criminal activities, scientific community, etc. Several link prediction methods exist which are applicable to specific types of networks. Here, the primary aim of this paper is to perform feature extraction from the given real-time complex network using subgraph extraction technique and labeling of the vertices in the subgraph according to the distance from the vertex associated with each target link. The vertices in the subgraph are labeled based on the Geometric mean distance and Arithmetic mean distance. This proposed model helps to learn the topological pattern from the extracted subgraph. The feature extraction is carried out with different size of the subgraph with the number of vertices as K = 10 and K = 15. These features are then fit into different machine learning classification models and deep learning convolutional neural network model. For the evaluation purpose, area under the receiver operating characteristic curve (AUC) metric is used. The AUC results obtained from all the classifiers have been shown. Further, the simulation results show that bagging and random forest achieved good performance. Finally, the comparative study is performed to summarize the results and proved that link prediction using classification models and deep learning model perform well across different kinds of complex networks. This solved the link prediction problem with superior performance and with robustness.

链接预测由于其在现实世界中在社交网络分析，生物医学，电子商务，犯罪活动，科学界等各个领域的适用性而在机器学习领域引起了极大的关注。存在几种链接预测方法，它们适用于特定类型的网络。在此，本文的主要目的是使用子图提取技术从给定的实时复杂网络中进行特征提取，并根据与每个目标链接相关联的顶点之间的距离对子图中的顶点进行标注。子图中的顶点是根据“几何平均距离”和“算术平均距离”标记的。提出的模型有助于从提取的子图中学习拓扑模式。K  = 10和K  =15。然后将这些特征拟合到不同的机器学习分类模型和深度学习卷积神经网络模型中。为了进行评估，使用了接收器工作特性曲线（AUC）指标下的面积。已经显示了从所有分类器获得的AUC结果。此外，仿真结果表明，套袋和随机森林均具有良好的性能。最后，进行比较研究以总结结果，并证明使用分类模型和深度学习模型的链接预测在不同类型的复杂网络中表现良好。这以优异的性能和鲁棒性解决了链路预测问题。