利用人工神经网络预测无标度网络中的隐藏链接和缺失节点
Predicting Hidden Links and Missing Nodes in Scale-Free Networks with Artificial Neural Networks
AI总结：
本文提出利用随机网络生成器和人工神经网络，预测无标度网络中的隐藏链接和缺失节点，通过区分不同子类型网络来识别异常。
摘要：
现实中的许多网络，如万维网、蛋白质相互作用网络、语义网络、航空网络、银行间支付网络等，都以无标度网络的形式存在。为了分析这些网络，了解无标度网络的性质是必要的。通过利用无标度网络的性质，可以识别网络中的任何异常。在本研究中，我们提出了一种方法，以算法的形式来预测无标度网络中的隐藏链接和缺失节点，其中我们结合了随机网络生成器作为训练数据源，以及人工神经网络进行监督分类。我们的目标是训练神经网络以区分不同子类型的无标度网络，并预测给定无标度网络中（存在和缺失）节点之间的缺失节点和隐藏链接。我们选择了Bela Bollobas的有向无标度随机图生成算法作为随机网络生成器，以生成大量无标度网络的数据。
英文摘要：
There are many networks in real life which exist as form of Scale-free networks such as World Wide Web, protein-protein interaction network, semantic networks, airline networks, interbank payment networks, etc. If we want to analyze these networks, it is really necessary to understand the properties of scale-free networks. By using the properties of scale free networks, we can identify any type of anomalies in those networks. In this research, we proposed a methodology in a form of an algorithm to predict hidden links and missing nodes in scale-free networks where we combined a generator of random networks as a source of train data, on one hand, with artificial neural networks for supervised classification, on the other, we aimed at training the neural networks to discriminate between different subtypes of scale-free networks and predicted the missing nodes and hidden links among (present and missing) nodes in a given scale-free network. We chose Bela Bollobas's directed scale-free random graph generation algorithm as a generator of random networks to generate a large set of scale-free network's data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
