Aziz Furqan, Slater Luke T, Bravo-Merodio Laura, Acharjee Animesh, Gkoutos Georgios V
School of Computing and Mathematical Sciences, University of Leicester, University Rd, Leicester, LE1 7RH, UK.
Centre for Health Data Science, Birmingham, B15 2WB, UK.
Sci Rep. 2023 Sep 5;13(1):14660. doi: 10.1038/s41598-023-41476-9.
Link prediction in complex networks has recently attracted a great deal of attraction in diverse scientific domains, including social and biological sciences. Given a snapshot of a network, the goal is to predict links that are missing in the network or that are likely to occur in the near future. This problem has both theoretical and practical significance; it not only helps us to identify missing links in a network more efficiently by avoiding the expensive and time consuming experimental processes, but also allows us to study the evolution of a network with time. To address the problem of link prediction, numerous attempts have been made over the recent years that exploit the local and the global topological properties of the network to predict missing links in the network. In this paper, we use parametrised matrix forest index (PMFI) to predict missing links in a network. We show that, for small parameter values, this index is linked to a heat diffusion process on a graph and therefore encodes geometric properties of the network. We then develop a framework that combines the PMFI with a local similarity index to predict missing links in the network. The framework is applied to numerous networks obtained from diverse domains such as social network, biological network, and transport network. The results show that the proposed method can predict missing links with higher accuracy when compared to other state-of-the-art link prediction methods.
复杂网络中的链接预测最近在包括社会科学和生物科学在内的各种科学领域引起了广泛关注。给定网络的一个快照，目标是预测网络中缺失的或可能在不久的将来出现的链接。这个问题具有理论和实际意义；它不仅有助于我们通过避免昂贵且耗时的实验过程更有效地识别网络中的缺失链接，还使我们能够研究网络随时间的演变。为了解决链接预测问题，近年来人们进行了大量尝试，利用网络的局部和全局拓扑特性来预测网络中的缺失链接。在本文中，我们使用参数化矩阵森林指数（PMFI）来预测网络中的缺失链接。我们表明，对于小参数值，该指数与图上的热扩散过程相关，因此编码了网络的几何特性。然后，我们开发了一个框架，将PMFI与局部相似性指数相结合来预测网络中的缺失链接。该框架应用于从社交网络、生物网络和交通网络等不同领域获得的众多网络。结果表明，与其他现有最先进的链接预测方法相比，所提出的方法能够以更高的准确率预测缺失链接。
