Performance improvement of topological similarity-based link prediction models becomes an important research in complex networks. In the models based on node influence, researchers mainly consider the roles of endpoints or neighbors. Through investigations, we find that an endpoint with large influence has many neighbors. Meanwhile, the neighbors connect with more nodes besides endpoint, meaning that the endpoint can transmit extensive influence by the powerful combination of itself and neighbors. In addition, we evaluate the node influence by degree because the degree represents the number of neighbors accurately. In this paper, through focusing on the degree of endpoints and neighbors, we propose the powerful combination of endpoints and neighbors (PCEN) model. Experiments on twelve real network datasets demonstrate that the proposed model has better prediction performances than the traditional models.

中文翻译：

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基于端点和邻居的强大组合的链路预测

基于拓扑相似性的链路预测模型的性能改进成为复杂网络中的一项重要研究。在基于节点影响的模型中，研究人员主要考虑端点或邻居的作用。通过调查，我们发现一个影响大的端点有很多邻居。同时，邻居节点除了端点外，还连接了更多的节点，这意味着端点可以通过自身与邻居的强强联合来传递广泛的影响。此外，我们通过度来评估节点影响，因为度准确地代表了邻居的数量。在本文中，通过关注端点和邻居的程度，我们提出了强大的端点和邻居组合（PCEN）模型。