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.
The application of degree related clustering coefficient in estimating the link predictability and predicting missing links of networks
Xing Chen, Ling Fang, Tinghong Yang, Jian Yang, Zerong Bao, Duzhi Wu, Jing Zhao; The application of degree related clustering coefficient in estimating the link predictability and predicting missing links of networks. Chaos 1 May 2019; 29 (5): 053135. https://doi.org/10.1063/1.5029866
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