网络结构扰动对层间链路预测的影响
Network structural perturbation against interlayer link prediction
摘要：
层间链路预测的目的是在复用网络的不同层上匹配相同的实体。现有的研究试图从网络结构、属性特征及其组合等方面进行更准确、更有效或更一般性的预测。很少有人分析层内链接的影响。也就是说，很少有工作研究的骨干结构，可以有效地保持预测的准确性，同时处理较少数量的层内链接。它可用于研究哪些类型的层内链接对于正确预测最重要。是否存在任何层内链接，其存在会导致预测性能比不存在时更差，以及如何以最小的成本攻击预测算法？为此，提出了两种网络结构摄动法。对于整个网络的结构信息完全已知的情况下，我们提供了一个全局扰动策略，赋予不同类型的层内链路不同的扰动权重，然后选择一个预定比例的层内链路删除根据权重。相反，如果这些信息不能一次获得，我们设计了一个有偏随机游走过程，局部扰动策略，执行扰动。四种层间链路预测算法在不同的真实世界和人工扰动的复用网络上进行。我们发现，与小度节点连接的层内链接对预测精度的影响最大。与大度节点连接的层内链路可能对层间链路预测产生副作用。
英文摘要：
Interlayer link prediction aims at matching the same entities across different layers of the multiplex network. Existing studies attempt to predict more accurately, efficiently, or generically from the aspects of network structure, attribute characteristics, and their combination. Few of them analyze the effects of intralayer links. Namely, few works study the backbone structures which can effectively preserve the predictive accuracy while dealing with a smaller number of intralayer links. It can be used to investigate what types of intralayer links are most important for correct prediction. Are there any intralayer links whose presence leads to worse predictive performance than their absence, and how to attack the prediction algorithms at the minimum cost? To this end, two kinds of network structural perturbation methods are proposed. For the scenario where the structural information of the whole network is completely known, we offer a global perturbation strategy that gives different perturbation weights to different types of intralayer links and then selects a predetermined proportion of intralayer links to remove according to the weights. In contrast, if these information cannot be obtained at one time, we design a biased random walk procedure, local perturbation strategy, to execute perturbation. Four kinds of interlayer link prediction algorithms are carried out on different real-world and artificial perturbed multiplex networks. We find out that the intralayer links connected with small degree nodes have the most significant impact on the prediction accuracy. The intralayer links connected with large degree nodes may have side effects on the interlayer link prediction.
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。
