Chen Xing, Fang Ling, Yang Tinghong, Yang Jian, Bao Zerong, Wu Duzhi, Zhao Jing
Fundamental Department, Army Logistic University of PLA, Chongqing 401311, China.
School of Pharmacy, Second Military Medical University, Shanghai 200433, China.
Chaos. 2019 May;29(5):053135. doi: 10.1063/1.5029866.
Though a lot of valuable algorithms of link prediction have been created, it is still difficult to improve the accuracy of link prediction for some networks. Such difficulties may be due to the intrinsic topological features of these networks. To reveal the correlation between the network topology and the link predictability, we generate a group of artificial networks by keeping some structural features of an initial seed network. Based on these artificial networks and some real networks, we find that five topological measures including clustering coefficient, structural consistency, random walk entropy, network diameter, and average path length significantly show their impact on the link predictability. Then, we define a topological score that combines these important topological features. Specifically, it is an integration of structural consistency with degree-related clustering coefficient defined in this work. This topological score exhibits high correlation with the link predictability. Finally, we propose an algorithm for link prediction based on this topological score. Our experiment on eight real networks verifies good performance of this algorithm in link prediction, which supports the reasonability of the new topological score. This work could be insightful for the study of the link predictability.
尽管已经创建了许多有价值的链接预测算法，但对于某些网络而言，提高链接预测的准确性仍然很困难。这些困难可能归因于这些网络的内在拓扑特征。为了揭示网络拓扑与链接可预测性之间的相关性，我们通过保留初始种子网络的一些结构特征来生成一组人工网络。基于这些人工网络和一些真实网络，我们发现包括聚类系数、结构一致性、随机游走熵、网络直径和平均路径长度在内的五个拓扑度量显著显示了它们对链接可预测性的影响。然后，我们定义了一个结合这些重要拓扑特征的拓扑分数。具体来说，它是结构一致性与本文中定义的与度相关的聚类系数的积分。这个拓扑分数与链接可预测性表现出高度相关性。最后，我们基于这个拓扑分数提出了一种链接预测算法。我们在八个真实网络上的实验验证了该算法在链接预测方面的良好性能，这支持了新拓扑分数的合理性。这项工作对于链接可预测性的研究可能具有启发性。
