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Integrating Local Closure Coefficient into Weighted Networks for Link Prediction

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Data Science (ICPCSEE 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1451))

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Abstract

Triadic closure is a simple and fundamental kind of link formulation mechanism in network. Local closure coefficient (LCC), a new network property, is to measure the triadic closure with respect to the fraction of length-2 paths for link prediction. In this paper, a weighted format of LCC (WLCC) is introduced to measure the weighted strength of local triadic structure, and a statistic similari-ty-based link prediction metric is proposed to incorporate the definition of WLCC. To prove the metrics effectiveness and scalability, the WLCC formula-tion was further investigated under weighted local Naive Bayes (WLNB) link prediction framework. Finally, extensive experimental studies was conducted with weighted baseline metrics on various public network datasets. The results demonstrate the merits of the proposed metrics in comparison with the weighted baselines.

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References

  1. Getoor, L., Diehl, C.P.: ACM SIGKDD Explor. Newslett. 7, 3–12 (2005)

    Google Scholar 

  2. Liben-Nowell, D., Kleinberg, J.: The link prediction problem for social networks. In: Proceedings of the Twelfth International Conference on Information and Knowledge Management, New Orleans, LA, USA, vol. 9999, pp. 556–559. ACM (2003)

    Google Scholar 

  3. Martinezctor, V., Berzal, F., Cubero, J.-C.: A survey of link prediction in complex networks. ACM Comput. Surv. 49, 1–33 (2016)

    Google Scholar 

  4. Li, S., Song, X., Lu, H., Zeng, L., Shi, M., Liu, F.: Friend recommendation for cross marketing in online brand community based on intelligent attention allocation link prediction algorithm. Expert Syst. Appl. 139, 112839 (2020)

    Article  Google Scholar 

  5. Benson, A.R., Abebe, R., Schaub, M.T., Jadbabaie, A., Kleinberg, J.: Proc. Nat. Acad. Sci. 115, E11221–E11230 (2018)

    Google Scholar 

  6. Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Proceedings of Advances in Neural Information Processing Systems, Montreal, Canada, pp. 4284–4295. ACM (2018)

    Google Scholar 

  7. Tay, Y., Luu, A.T., Hui, S.C., Brauer, F.: Random semantic tensor ensemble for scalable knowledge graph link prediction. In: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining, Cambridge, UK, p. 751760. ACM (2017)

    Google Scholar 

  8. Lv, L., Zhou, T.: Link prediction in complex networks: a survey. Physica A Stat. Mech. Appl. 390, 1150–1170 (2011)

    Google Scholar 

  9. Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Netw. 25, 211–230 (2003)

    Article  Google Scholar 

  10. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18, 39–43 (1953)

    Article  Google Scholar 

  11. Lv, L., Jin, C.-H., Zhou, T.: Similarity index based on local paths for link prediction of complex networks. Phys. Rev. E 80, 046122 (2009)

    Article  Google Scholar 

  12. Liu, W., Lu, L.: Link prediction based on local random walk. EPL (Europhys. Lett.) 89, 58007 (2010)

    Google Scholar 

  13. Wang, L., Ren, J., Xu, B., et al.: MODEL: motif-based deep feature learning for link prediction. IEEE Trans. Comput. Soc. Syst. 7(2), 503–516 (2020)

    Article  Google Scholar 

  14. Manuel, C.: Return random walks for link prediction. Inf. Sci. 51, 99–107 (2020)

    Google Scholar 

  15. Wang, W., Cai, F., Jiao, P., Pan, L.: A perturbation-based framework for link prediction via non-negative matrix factorization. Sci. Rep. 6, 38938 (2016)

    Article  Google Scholar 

  16. Pech, R., Hao, D., Pan, L., Cheng, H., Zhou, T.: Link prediction via matrix completion. EPL (Europhys. Lett.) 117, 38002 (2017)

    Google Scholar 

  17. Yin, H., Benson, A.R., Leskovec, J.: The local closure coefficient: a new perspective on network clustering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, Melbourne, Australia, pp. 303–311. ACM (2019)

    Google Scholar 

  18. Liu, Y., Li, T., Xu, X.: Link prediction by multiple motifs in directed networks. IEEE Access 8, 174–183 (2019)

    Article  Google Scholar 

  19. Lv, L., Zhou, T.: Link prediction in weighted networks: the role of weak ties. EPL (Europhys. Lett.) 89, 18001 (2010)

    Google Scholar 

  20. Wu, J., Zhou, B., Shen, J.: Algorithm of integrating local weighted clustering coefficients for link prediction. Appl. Res. Comput. 12, 15 (2018)

    Google Scholar 

  21. Wu, J., Zhang, G., Ren, Y., Zhang, X., Yang, Q.: Weighted local naive bayes link prediction. J. Inf. Process. Syst. 13, 914–927 (2017)

    Google Scholar 

  22. Rafiee, S., Salavati, C., Abdollahpouri, A.: CNDP: link prediction based on common neighbors degree penalization. Physica A Stat. Mech. Appl. 539, 122950 (2020)

    Article  Google Scholar 

  23. Murata, T., Moriyasu, S.: Link prediction of social networks based on weighted proximity measures. In: Proceedings of International Conference on Web Intelligence, California, USA, pp. 85–88. IEEE/WIC/ACM (2007)

    Google Scholar 

  24. Zhu, B., Xia, Y.: Link prediction in weighted networks: a weighted mutual information model. PloS ONE 11, e0148265 (2016)

    Article  Google Scholar 

  25. De Sa, H.R., Prudencio, R.B.C.: Supervised link prediction in weighted networks. In: Proceedings of the 2011 International Joint Conference on Neural Networks, San Jose, California, USA, pp. 2281–2288. IEEE (2011)

    Google Scholar 

  26. Moradabadi, B., Meybodi, M.R.: Link prediction in weighted social networks using learning automata. Eng. Appl. Artif. Intell. 70, 16–24 (2018)

    Article  Google Scholar 

  27. Yin, H., Benson, A.R., Ugander, J.: Measuring directed triadic closure with closure coefficients. arXiv preprint arXiv:1905.10683 (2019)

  28. Liu, Z., Zhang, Q.-M., Lu, L., Zhou, T.: Link prediction in complex networks: a local naive bayes model. EPL (Europhys. Lett.) 96, 48007 (2011)

    Google Scholar 

  29. Pan, R.K., Kaski, K., Fortunato, S.: World citation and collaboration networks: uncovering the role of geography in science. Sci. Rep. 2, 902 (2012)

    Article  Google Scholar 

  30. Kunegis, J.: KONECT: the Koblenz network collection. In: Proceedings of international Conference on World Wide Web, Seoul, Korea, pp. 1343–1350. ACM (2011)

    Google Scholar 

  31. Harper, F.M., Konstan, J.A.: The movielens datasets: history and context. ACM Trans. Interact. Intell. Syst. (TIIS) 5, 1–19 (2015)

    Google Scholar 

  32. Kumar, S., Hooi, B., Makhija, D., Kumar, M., Faloutsos, C., Subrahmanian, V.S.: Rev2: fraudulent user prediction in rating platforms. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, California, USA, pp. 333–341. ACM (2018)

    Google Scholar 

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Acknowledgements

This work is supported by Basic and Applied Basic Research Foundation of Guangdong Province (No. 2020A1515011495) and Guangzhou Science and Technology Foundation Project (No. 202002030266).

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Authors and Affiliations

  1. Guangdong Polytechnic of Industry Commerce, Guangzhou, 510510, GD, China

    JieHua Wu

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  1. JieHua Wu
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Editor information

Editors and Affiliations

  1. North University of China, Taiyuan, China

    Jianchao Zeng

  2. North University of China, Taiyuan, China

    Pinle Qin

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Wu, J. (2021). Integrating Local Closure Coefficient into Weighted Networks for Link Prediction. In: Zeng, J., Qin, P., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2021. Communications in Computer and Information Science, vol 1451. Springer, Singapore. https://doi.org/10.1007/978-981-16-5940-9_4

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  • DOI: https://doi.org/10.1007/978-981-16-5940-9_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-5939-3

  • Online ISBN: 978-981-16-5940-9

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