Impact of endpoint structure attributes on local information algorithms based on link prediction

The structural similarity based link prediction algorithms mainly exploit the information of network topology, such as links and nodes, to predict the potential links in complex networks. Among these algorithms, the local information similarity based algorithms have attracted the extensive attentions from the majority of researchers due to their low complexity and general applicability. The algorithms mainly exploit the attributes of common neighbors on the second-order transmission paths to predict the connection probability between the unconnected nodes, but ignore the structure attributes of endpoints. The structure attributes of an endpoint can be quantified as its influence resources, which make an important contribution to link prediction. To heighten the performances of local information based algorithms, this paper exploits the different structure attributes of endpoints to express the influence resources, and explores the contributions of the different endpoint attributes to local information algorithms. Extensive simulations on 12 real benchmark datasets show that, in most cases, the node degree expressing the influence resource makes the greatest contribution to improve the performances of the local information algorithms. Specifically, DCN, DAA and DRA algorithm possess the best prediction performances in 9, 5 and 7 datasets, respectively. Furthermore, compared with 6 mainstream algorithms, DRA as the best improved algorithm shows the optimal prediction performances in 8 datasets.

Automated summary

This paper investigates the structural similarity based link prediction algorithms and proposes a method to enhance the performance of local information algorithms by utilizing different structure attributes of endpoints. Experimental results show that the node degree contributes the most to improving the algorithm performance.