The problem of determining the likelihood of the existence of a link between two nodes in a network is called link prediction. This is made possible thanks to the existence of a topological structure in most real-life networks. In other words, the topologies of networked systems such as the World Wide Web, the Internet, metabolic networks, and human society are far from random, which implies that partial observations of these networks can be used to infer information about undiscovered interactions. Significant research efforts have been invested into the development of link prediction algorithms, and some researchers have made the implementation of their methods available to the research community. These implementations, however, are often written in different languages and use different modalities of interaction with the user, which hinders their effective use. This paper introduces LinkPred, a high-performance parallel and distributed link prediction library that includes the implementation of the major link prediction algorithms available in the literature. The library can handle networks with up to millions of nodes and edges and offers a unified interface that facilitates the use and comparison of link prediction algorithms by researchers as well as practitioners.

中文翻译：

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LinkPred：高性能的库，用于复杂网络中的链路预测

确定网络中两个节点之间存在链路的可能性的问题称为链路预测。由于大多数现实生活中的网络中都存在拓扑结构，因此使之成为可能。换句话说，诸如万维网，互联网，新陈代谢网络和人类社会之类的网络系统的拓扑结构并不是随机的，这意味着对这些网络的部分观察可以用来推断有关未发现的相互作用的信息。链接预测算法的开发已投入大量研究工作，一些研究人员已将其方法的实现提供给研究团体。但是，这些实现通常是用不同的语言编写的，并使用与用户互动的不同方式，这阻碍了它们的有效使用。本文介绍LinkPred，这是一种高性能的并行和分布式链接预测库，其中包括文献中可用的主要链接预测算法的实现。该库可以处理具有多达数百万个节点和边缘的网络，并提供统一的接口，以方便研究人员和从业人员使用和比较链路预测算法。