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

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Abstract

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.

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Data Availability

The data used to support the findings of the study are as follows: http://vlado.fmf.uni-lj.si/pub/networks/data/http://snap.stanford.edu/data/index.html.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61471060).

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Authors and Affiliations

  1. State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yang Tian, Gaofeng Nie & Hui Tian

  2. School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Qimei Cui

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  1. Yang Tian
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  2. Gaofeng Nie
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  3. Hui Tian
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Correspondence to Hui Tian.

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Tian, Y., Nie, G., Tian, H. et al. Impact of endpoint structure attributes on local information algorithms based on link prediction. Computing 105, 115–129 (2023). https://doi.org/10.1007/s00607-022-01115-z

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