ABSTRACT
In the basic random walk link prediction method, the probability of a walking particle when selecting a neighbor node for a walk is determined only by the degree of the current node, and it is fixed and uniform, without considering the impact of degree of the neighboring nodes on the transition probability. In view of this, a link prediction algorithm is proposed in which the degrees of the current node and its neighbor nodes jointly determine the transition probability. First, using the transition probability model of Metropolis-Hasting Random Walk (MHRW) algorithm to redefine the transition probability of the walking particles between the neighbor nodes, then combining Random Walk with Restart (RWR) similarity index to propose the Metropolis-Hasting Random Walk with Restart (MHRWR) algorithm in this paper for link prediction. The link prediction comparison experiments been performed on 6 different scale real network datasets. Compared with the benchmark algorithm, the MHRWR algorithm not only improved the AUC index, but also improved the Precision and Ranking score; compared with the RWR algorithm, the AUC value has increased by an average of 2.10%, and the highest is 5.34%. Experimental results show that the MHRWR algorithm of our proposed leads to superior accuracy in link prediction.
- Wang, Shasha, Yuxian Du, and Yong Deng. "A new measure of identifying influential nodes: Efficiency centrality." Communications in Nonlinear Science and Numerical Simulation 47 (2017): 151--163.Google ScholarCross Ref
- Grover, Aditya, and Jure Leskovec. "node2vec: Scalable feature learning for networks." Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining. 2016. DOI=https://doi.org/10.1145/2939672.2939754 Google Scholar
- Wang, Zhiqiang, Jiye Liang, and Ru Li. "A fusion probability matrix factorization framework for link prediction." Knowledge-Based Systems 159 (2018): 72--85.Google ScholarCross Ref
- Cao, Zhu, Linlin Wang, and Gerard De Melo. "Link prediction via subgraph embedding-based convex matrix completion." Thirty-Second AAAI Conference on Artificial Intelligence. 2018.Google Scholar
- Yin, Likang, et al. "An evidential link prediction method and link predictability based on Shannon entropy." Physica A: Statistical Mechanics and its Applications 482 (2017): 699--712.Google ScholarCross Ref
- Wang, Shasha, Yuxian Du, and Yong Deng. "A new measure of identifying influential nodes: Efficiency centrality." Communications in Nonlinear Science and Numerical Simulation 47 (2017): 151--163.Google ScholarCross Ref
- Jiao, Pengfei, et al. "Link predication based on matrix factorization by fusion of multi class organizations of the network." Scientific reports 7.1 (2017): 1--12.Google ScholarCross Ref
- Luo, Peng, Chong Wu, and Yongli Li. "Link prediction measures considering different neighbors' effects and application in social networks." International Journal of Modern Physics C 28.03 (2017): 1750033.Google ScholarCross Ref
- Zhang, Cheng-Jun, and An Zeng. "Prediction of missing links and reconstruction of complex networks." International Journal of Modern Physics C 27.10 (2016): 1650120. DIO=https://doi.org/10.1142/S0129183116501205Google ScholarCross Ref
- Zhang, Peng, et al. "Measuring the robustness of link prediction algorithms under noisy environment." Scientific reports 6 (2016): 18881.Google ScholarCross Ref
- Liu, Yangyang, et al. "The degree-related clustering coefficient and its application to link prediction." PhysicaA: Statistical Mechanics and Its Applications 454 (2016): 24--33.Google ScholarCross Ref
- Zhang Yuexia, and Yixuan Feng. "A Summary of the Methods and Development of Link Prediction," Measurement & Control Technology 38.2 (2019): 8--12.Google Scholar
- Mikolov, Tomas, et al. "Distributed representations of words and phrases and their compositionality." Advances in neural information processing systems. 2013. Google Scholar
- Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. "Deepwalk: Online learning of social representations." Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. 2014. Google Scholar
- Gjoka, Minas, et al. "Walking in facebook: A case study of unbiased sampling of osns." 2010 Proceedings IEEE Infocom. Ieee, 2010. Google Scholar
- Wang, W. T., et al. "Network representation learning algorithm based on improved random walk." Journal of Computer Applications 39.3 (2019): 651--655.Google Scholar
- Liu, S., et al. "Link prediction algorithm based on network representation learning and random walk." J. Comput. Appl 37.08 (2017): 2234--2239.Google Scholar
- Jin, Woojeong, Jinhong Jung, and U. Kang. "Supervised and extended restart in random walks for ranking and link prediction in networks." PloS one 14.3 (2019): e0213857-e0213857.Google ScholarCross Ref
- Lü Yanan., et al. "Link Prediction Algorithm Based on Biased Random Walk with Restart." Complex Systems and Complexity Science 15.4 (2019): 17--24.Google Scholar
- Tong, Hanghang, Christos Faloutsos, and Jia-Yu Pan. "Fast random walk with restart and its applications." Sixth international conference on data mining (ICDM'06). IEEE, 2006. Google Scholar
- Lorrain, Francois, and Harrison C. White. "Structural equivalence of individuals in social networks." The Journal of mathematical sociology 1.1 (1971): 49--80.Google ScholarCross Ref
- Adamic, Lada A., and Eytan Adar. "Friends and neighbors on the web." Social networks 25.3 (2003): 211--230.Google ScholarCross Ref
- Zhou, Tao, Linyuan Lü, and Yi-Cheng Zhang. "Predicting missing links via local information." The European Physical Journal B 71.4 (2009): 623--630.Google ScholarCross Ref
- Katz, Leo. "A new status index derived from sociometric analysis." Psychometrika 18.1 (1953): 39--43.Google ScholarCross Ref
- Klein, Douglas J., and Milan Randić. "Resistance distance." Journal of mathematical chemistry 12.1 (1993): 81--95.Google ScholarCross Ref
- Hanley, James A., and Barbara J. McNeil. "The meaning and use of the area under a receiver operating characteristic (ROC) curve." Radiology 143.1 (1982): 29--36.Google ScholarCross Ref
- Herlocker, Jonathan L., et al. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22.1 (2004): 5--53. Google ScholarDigital Library
- Zhou, Tao, et al. "Bipartite network projection and personal recommendation." Physical review E 76.4 (2007): 046115.Google ScholarCross Ref
- Fawcett, Tom. "An introduction to ROC analysis." Pattern recognition letters 27.8 (2006): 861--874. Google ScholarDigital Library
- Li, Longjie, et al. "Effective link prediction based on community relationship strength." IEEE Access 7 (2019): 43233--43248.Google ScholarCross Ref
- Lusseau, David, et al. "The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations." Behavioral Ecology and Sociobiology 54.4 (2003): 396--405.Google ScholarCross Ref
- Watts, Duncan J., and Steven H. Strogatz. "Collective dynamics of 'small-world' networks." nature 393.6684 (1998): 440.Google Scholar
- Adamic, Lada A., and Natalie Glance. "The political blogosphere and the 2004 US election: divided they blog." Proceedings of the 3rd international workshop on Link discovery. 2005. Google Scholar
Index Terms
- An Improved Link Prediction Algorithm Based on Comprehensive Consideration of Joint Influence of Adjacent Nodes for Random Walk with Restart
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