Abstract
In social networks the behavior of individuals can be researched through the evolution of the microstructure. As we know, triad is the basic atom shape to build the whole social network. However we find that quad plays the basic role rather than triad in Enterprise Network (EN). In particular, we focus on four typical microstructure patterns including triad, 4-cycle, 4-chordalcycle and 4-clique in EN. We propose algorithms to mine these microstructure patterns and compute the frequencies of each type of microstructure patterns in an efficient parallel way. We also analyze the structural features of these microstructure patterns in a perspective of ego network. Additionally we present the evolutionary rules between these microstructure patterns based on the statistical analysis. Finally we combine the features into traditional methods to solve the link prediction problem. The results show that these features and our combination methods are effective to predict links between enterprises in EN.
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Social structure. https://en.wikipedia.org/wiki/Social_structure
Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N.G., Willke, T.L.: Graphlet decomposition: framework, algorithms, and applications. Knowl. Inf. Syst. 50(3), 689–722 (2017)
Graphlets. https://en.wikipedia.org/wiki/Graphlets
Bhuiyan, M.A., Rahman, M., Rahman, M., Al Hasan, M.: Guise: uniform sampling of graphlets for large graph analysis. In: 2012 IEEE 12th International Conference on Data Mining, pp. 91–100. IEEE (2012)
Biswas, A., Biswas, B.: Investigating community structure in perspective of ego network. Expert Syst. Appl. 42(20), 6913–6934 (2015)
Dunbar, R., Arnaboldi, V., Conti, M., Passarella, A.: The structure of online social networks mirrors those in the offline world. Soc. Netw. 43, 39–47 (2015)
Dong, Y., Tang, J., Wu, S., Tian, J., Chawla, N.V., Rao, J., Cao, H.: Link prediction and recommendation across heterogeneous social networks. In: 2012 IEEE 12th International Conference on Data Mining, pp. 181–190. IEEE (2012)
Girard, Y., Hett, F., Schunk, D.: How individual characteristics shape the structure of social networks. J. Econ. Behav. Organ. 115, 197–216 (2015)
Gordon, I.R., McCann, P.: Industrial clusters: complexes, agglomeration and/or social networks? Urban stud. 37(3), 513–532 (2000)
Huang, H., Tang, J., Wu, S., Liu, L., et al.: Mining triadic closure patterns in social networks. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 499–504. ACM (2014)
Li, S., Daie, P.: Configuration of assembly supply chain using hierarchical cluster analysis. Procedia CIRP 17, 622–627 (2014)
Lou, T., Tang, J., Hopcroft, J., Fang, Z., Ding, X.: Learning to predict reciprocity and triadic closure in social networks. ACM Trans. Knowl. Disc. Data (TKDD) 7(2), 5 (2013)
Madhavan, R., Gnyawali, D.R., He, J.: Two’s company, three’s a crowd? Triads in cooperative-competitive networks. Acad. Manag. J. 47(6), 918–927 (2004)
Toral, S., Martínez-Torres, M.D.R., Barrero, F.: Analysis of virtual communities supporting OSS projects using social network analysis. Inf. Softw. Technol. 52(3), 296–303 (2010)
Trpevski, I., Dimitrova, T., Boshkovski, T., Kocarev, L.: Graphlet characteristics in directed networks. arXiv preprint arXiv:1603.05843 (2016)
Wang, L., Liu, S., Pan, L., Wu, L., Meng, X.: Enterprise relationship network: build foundation for social business. In: 2014 IEEE International Congress on Big Data, pp. 347–354. IEEE (2014)
Wasserman, S., Pattison, P.: Logit models and logistic regressions for social networks: I. An introduction to markov graphs andp. Psychometrika 61(3), 401–425 (1996)
Yanardag, P., Vishwanathan, S.: Deep graph kernels. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1365–1374. ACM (2015)
Stager, M., Lukowicz, P., Troster, G.: Dealing with class skew in context recognition. In: 26th IEEE International Conference on Distributed Computing Systems Workshops (ICDCSW 2006), pp. 58–58. IEEE (2006)
Salakhutdinov, R., Mnih, A.: Probabilistic matrix factorization. In: NIPS, vol. 20, pp. 1–8 (2011)
Lawrence, N.D., Urtasun, R.: Non-linear matrix factorization with gaussian processes. In: Proceedings of the 26th Annual International Conference on Machine Learning, pp. 601–608. ACM (2009)
Jeh, G., Widom, J.: SimRank: a measure of structural-context similarity. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 538–543. ACM (2002)
Acknowledgment
The authors would like to acknowledge the support provided by the National Natural Science Foundation of China (61402263, 91546203), the National Key Research and Development Program of China (2016YFB0201405), the Fundamental Research Funds of Shandong University (2016JC011), the Natural Science Foundation of Shandong Province (ZR2014FQ031), the Shandong Provincial Science and Technology Development Program (2016GGX101008, 2016ZDJS01A09), the special funds of Taishan scholar construction project and the Taishan Industrial Experts Programme of Shandong Province No. tscy20150305.
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Shi, X., Wang, L., Liu, S., Wang, Y., Pan, L., Wu, L. (2017). Investigating Microstructure Patterns of Enterprise Network in Perspective of Ego Network. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10366. Springer, Cham. https://doi.org/10.1007/978-3-319-63579-8_34
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DOI: https://doi.org/10.1007/978-3-319-63579-8_34
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