SummaryIn recent years, social network analysis has received a lot of interest. A critical area of research in this field is link prediction. Link prediction is researched for other forms of social networks. Still, because social link networks (SLNs) change over time and depend on the discussed topics, this network has unique difficulties. Recent studies have focused on three main issues: extending link prediction to a dynamic environment, forecasting formation, and destroying network linkages that change over time. Although it is a challenging issue, deep learning (DL) techniques have been demonstrated to increase prediction accuracy significantly. This research proposes a novel approach to link correlation for social networks based on DL architectures in feature vector prediction and classification. Here the input data has been processed for smoothening and normalization with noise removal. Then, the feature vector was predicted using a dynamically structured convolutional radial basis neural network for this data. The expected feature vector has been classified using a stochastic gradient‐based graph neural network. The experimental analysis is carried out for various social network data in terms of accuracy of 98%, precision of 85%, recall of 86%, F‐1 score of 75%, AUC of 72%, and RMSE of 76%.

中文翻译：

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基于社交网络的链接关联，使用具有深度学习架构的图神经网络进行特征向量预测和分类

总结近年来，社交网络分析引起了人们的广泛兴趣。该领域的一个关键研究领域是链接预测。针对其他形式的社交网络研究链接预测。尽管如此，由于社交链接网络（SLN）会随着时间的推移而变化并且取决于讨论的主题，因此该网络具有独特的困难。最近的研究集中在三个主要问题：将链接预测扩展到动态环境、预测形成以及破坏随时间变化的网络链接。尽管这是一个具有挑战性的问题，但深度学习（DL）技术已被证明可以显着提高预测准确性。这项研究提出了一种基于特征向量预测和分类中的深度学习架构的社交网络链接相关性的新方法。这里，输入数据已被处理以进行平滑和归一化并消除噪声。然后，使用动态结构的卷积径向基神经网络对该数据预测特征向量。使用基于随机梯度的图神经网络对预期特征向量进行分类。对各种社交网络数据进行实验分析，准确率为98%，精确率为85%，召回率为86%，F-1得分为75%，AUC为72%，RMSE为76%。