A great variety of artificial and natural systems can be abstracted into a set of entities interacting with each other. Such abstractions can very well represent the underlying dynamics of the system when modeled as the network of vertices coupled by edges. Prediction of dynamics in these structures based on topological attribute or dependency relations is an important task. Link Prediction in such complex networks is regarded useful in almost all types of networks as it can be used to extract missing information, identify spurious interactions, and evaluate network evolving mechanisms. Various similarity and likelihood-based indices have been employed to infer different topological and relation-based information to form a link prediction algorithm. These algorithms, however, are too specific to the domain and do not encapsulate the generic nature of the real-world information. In most natural and engineered systems, the entities are linked with multiple types of associations and relations which play a factor in the dynamics of the network. It forms multiple subsystems or multiple layers of networked information. These networks are regarded as Multiplex Networks. This work presents an approach for link prediction in Multiplex networks where the associations are learned from the multiple layers of networks for link prediction purposes. Most of the real-world networks are represented as weighted networks. Weight prediction coupled with Link Prediction can be of great use. Link scores are received using various similarity measures and used to predict weights. This work further proposes and testifies a strategy for weight prediction. This work successfully proposes an algorithm for Weight Prediction using Link similarity measures on multiplex networks. The predicted weights show very less deviation from their actual weights. In comparison to other indices, the proposed method has a far low error rate and outperforms them concerning the metric performance NRMSE.

中文翻译：

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加权多路复用网络中链路预测的有效方法。

各种各样的人工和自然系统可以抽象为一组相互交互的实体。当被建模为由边耦合的顶点网络时，这种抽象可以很好地表示系统的底层动态。基于拓扑属性或依赖关系预测这些结构的动力学是一项重要任务。这种复杂网络中的链路预测在几乎所有类型的网络中都被认为是有用的，因为它可以用来提取缺失的信息、识别虚假交互以及评估网络演化机制。已经采用了各种基于相似性和似然性的指标来推断不同的拓扑和基于关系的信息，以形成链接预测算法。然而，这些算法，对领域来说太具体了，并且没有封装真实世界信息的一般性质。在大多数自然和工程系统中，实体与多种类型的关联和关系相关联，这些关联和关系在网络的动态中发挥作用。它形成了多个子系统或多层网络信息。这些网络被视为多路复用网络。这项工作提出了一种在多路复用网络中进行链路预测的方法，其中从多层网络中学习关联以进行链路预测。大多数现实世界的网络都表示为加权网络。权重预测与链接预测相结合会很有用。使用各种相似性度量接收链接分数并用于预测权重。这项工作进一步提出并验证了一种权重预测策略。这项工作成功地提出了一种在多路网络上使用链路相似性度量的权重预测算法。预测的权重与实际权重的偏差非常小。与其他指标相比，所提出的方法具有非常低的错误率，并且在度量性能 NRMSE 方面优于它们。