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Credible seed identification for large-scale structural network alignment

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Abstract

Structural network alignment utilizes the topological structure information to find correspondences between nodes of two networks. Researchers have proposed a line of useful algorithms which usually require a prior mapping of seeds acting as landmark points to align the rest nodes. Several seed-free algorithms are developed to solve the cold-start problem. However, existing approaches suffer high computational cost and low reliability, limiting their applications to large-scale network alignment. Moreover, there is a lack of useful metrics to quantify the credibility of seed mappings. To address these issues, we propose a credible seed identification framework and develop a metric to assess the reliability of a mapping. To tackle the cold-start problem, we employ graph embedding techniques to represent nodes by structural feature vectors in a latent space. We then leverage point set registration algorithms to match nodes algebraically and obtain an initial mapping of nodes. Besides, we propose a heuristic algorithm to improve the credibility of the initial mapping by filtering out mismatched node pairs. To tackle the computational problem in large-scale network alignment, we propose a divide-and-conquer scheme to divide large networks into smaller ones and then match them individually. It significantly improves the recall of mapping results. Finally, we conduct extensive experiments to evaluate the effectiveness and efficiency of our new approach. The results illustrate that the proposed method outperforms the state-of-the-art approaches in terms of both effectiveness and efficiency.

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Acknowledgements

The research presented in this paper is supported in part by National Natural Science Foundation (Nos. 61602370, 61672026, 61772411, U1736205), Postdoctoral Foundation (Nos. 201659M2806, 2018T111066), Fundamental Research Funds for the Central Universities (Nos. 1191320006, PY3A022), Shaanxi Postdoctoral Foundation, Project JCYJ20170816100819428 supported by SZSTI, CCF-NSFOCUS KunPeng Research Fund (No. CCF-NSFOCUS 2018006).

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Authors and Affiliations

  1. School of Software Engineering, Xi’an Jiaotong University, Xi’an, China

    Chenxu Wang, Yang Wang, Zhiyuan Zhao & Dong Qin

  2. MoE Key Lab of INNS, Xi’an Jiaotong University, Xi’an, China

    Chenxu Wang & Tao Qin

  3. Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China

    Xiapu Luo

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  1. Chenxu Wang
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Correspondence to Chenxu Wang.

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Wang, C., Wang, Y., Zhao, Z. et al. Credible seed identification for large-scale structural network alignment. Data Min Knowl Disc 34, 1744–1776 (2020). https://doi.org/10.1007/s10618-020-00699-4

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