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SSDBA: the stretch shrink distance based algorithm for link prediction in social networks

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Abstract

In the field of social network analysis, Link Prediction is one of the hottest topics which has been attracted attentions in academia and industry. So far, literatures for solving link prediction can be roughly divided into two categories: similarity-based and learning-based methods. The learning-based methods have higher accuracy, but their time complexities are too high for complex networks. However, the similarity-based methods have the advantage of low time consumption, so improving their accuracy becomes a key issue. In this paper, we employ community structures of social networks to improve the prediction accuracy and propose the stretch shrink distance based algorithm (SSDBA). In SSDBA, we first detect communities of a social network and identify active nodes based on community average threshold (CAT) and node average threshold (NAT) in each community. Second, we propose the stretch shrink distance (SSD) model to iteratively calculate the changes of distances between active nodes and their local neighbors. Finally, we make predictions when these links’ distances tend to converge. Furthermore, extensive parameters learning have been carried out in experiments. We compare our SSDBA with other popular approaches. Experimental results validate the effectiveness and efficiency of proposed algorithm.

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Acknowledgements

This work was partly supported by the National Natural Science Foundation of China (Grant Nos. 11671400, 61672524) and National Science Foundation (1747818).

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, China

    Ruidong Yan, Deying Li & Yongcai Wang

  2. Department of Computer Science, University of Texas at Dallas, Richardson, Texas, 75080, USA

    Yi Li & Weili Wu

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  1. Ruidong Yan
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  2. Yi Li
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  3. Deying Li
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  4. Weili Wu
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  5. Yongcai Wang
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Correspondence to Deying Li.

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Ruidong Yan received the BS degree in Information and Computing Sciences from Inner Mongolia University, China in 2014, He is a PhD candidate in the Department of Computer Science at Renmin University of China, China. His research interests include social networks, algorithm design and analysis.

Yi Li received her MS degree in Digital Communication and Multimedia from University of Texas at Dallas, USA. She is a PhD candidate in the Department of Computer Science in University of Texas at Dallas, USA. Her research area include social influence maximization/minimization and rumor blocking.

Deying Li is a professor of Renmin University of China, China. She received the BS degree and MS degree in Mathematics from Huazhong Normal University, China in 1985 and 1988, respectively. She obtained the PhD degree in Computer Science from City University of Hong Kong, China in 2004. Her research interests include wireless networks, ad hoc & sensor networks mobile computing, distributed network system, social networks, and algorithm design etc.

Weili Wu received the PhD and MS degrees from the Department of Computer Science, University of Minnesota, USA in 2002 and 1998, respectively. She is currently a full professor with the Department of Computer Science, The University of Texas at Dallas, USA. Her current research interests include data communication, data management, the design and analysis of algorithms for optimization problems that occur in wireless networking environments, and various database systems.

Yongcai Wang received BS and PhD degrees from department of automation sciences and engineering, Tsinghua University, China in 2001 and 2006. He worked as associated researcher at NEC Labs. China from 2007–2009. He was an research scientist in Institute for Interdisciplinary Information Sciences, Tsinghua University, China from 2009–2015. He was a visting scholar at Cornell University, USA in 2015. He is currently associate professor at Department of Computer Sciences, Renmin University of China, China. His research interests include network localization algorithms, internet of things, combinatorial optimization and applications.

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Yan, R., Li, Y., Li, D. et al. SSDBA: the stretch shrink distance based algorithm for link prediction in social networks. Front. Comput. Sci. 15, 151301 (2021). https://doi.org/10.1007/s11704-019-9083-3

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