Link prediction is a fundamental task in network theory due to the wide variety of applications in different domains. The objective of link prediction is to find the future links that are likely to be seen in some future time. In this paper, we propose a novel embedding-based technique that utilizes the concept of the Skip-gram framework. An embedding-based method embodies the learning of feature representations of nodes or links in a network. Our method jointly exploits the Skip-gram framework and max aggregator for edge embedding tasks. To test the effectiveness of the proposed method, we have conducted experiments on large size real-world networks. In the experimental evaluation, we have compared the proposed method against both similarity-based and learning-based approaches. The experimental results indicate the effectiveness of the proposed method both in terms of time and accuracy.

由于在不同领域的广泛应用，链路预测是网络理论中的一项基本任务。链接预测的目标是找到可能在未来某个时间看到的未来链接。在本文中，我们提出了一种新的基于嵌入的技术，它利用了 Skip-gram 框架的概念。基于嵌入的方法体现了对网络中节点或链接的特征表示的学习。我们的方法联合利用 Skip-gram 框架和最大聚合器进行边缘嵌入任务。为了测试所提出方法的有效性，我们在大型现实世界网络上进行了实验。在实验评估中，我们将所提出的方法与基于相似性和基于学习的方法进行了比较。