Abstract
Inferring the structure of an unknown network is a difficult problem of interest to researchers, academics , and industrialists . We develop a novel algorithm to infer nodes and edges in an unknown network. Our algorithm utilizes monitors that detect incident edges and adjacent nodes with their labels and degrees. The algorithm infers the network through a preferential random walk with a probabilistic restart at a previously discovered but unmonitored node, or a random teleportation to an unexplored node. Our algorithm outperforms random walk inference and random placement of monitors inference in edge discovery in all test cases. Our algorithm outperforms both methodologies in node inference in synthetic test networks; on real networks it outperforms them in the beginning of the inference. Finally, a website was created where these algorithms can be tested live on preloaded networks or custom networks as desired by the user. The visualization also displays the network as it is being inferred, and provides other statistics about the real and inferred networks.
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References
Adamic, L.A., Lukose, R.M., Puniyani, A.R., Huberman, B.A.: Search in power-law networks. Phys. Rev. E 64(4), 046135 (2001)
Biernacki, Patrick: Waldorf, Dan: Snowball sampling: problems and techniques of chain referral sampling. Sociol. Methods Res. 10(2), 141–163 (1981)
Bliss, C.A., Danforth, C.M., Dodds, P.S.: Estimation of global network statistics from incomplete data. PloS One 9(10), e108471 (2014)
Gera, R.: Network discovery visualization: an analysis of network discovery (2015). http://faculty.nps.edu/rgera/projects
Leskovec, J., Faloutsos, C.: Sampling from large graphs. In: Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 631–636. ACM (2006)
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford large network dataset collection (2014). http://snap.stanford.edu/data
Middendorf, M., Ziv, E., Wiggins, C.H.: Inferring network mechanisms: the drosophila melanogaster protein interaction network. Proc. Natl. Acad. Sci. U.S.A. 102(9), 3192–3197 (2005)
Miyano, E., Ono, H.: Maximum domination problem. In: Proceedings of the Seventeenth Computing: The Australasian Theory Symposium, vol. 119, pp. 55–62. Australian Computer Society Inc (2011)
Newman, Mark: Networks: An Introduction. Oxford University Press Inc, New York (2010)
Ore, O.: Theory of graphs. Am. Math. Soc. Colloq. Publ. 38, (1962)
Shandilya, S.G., Timme, M.: Inferring network topology from complex dynamics. New J. Phys. 13(1), 013004 (2011)
Sharma, R., Magnani, M., Montesi, D.: Missing data in multiplex networks: a preliminary study. In: Third International Workshop on Complex Networks and their Applications (2014)
Sharma, R., Magnani, M., Montesi, D.: Investigating the types and effects of missing data in multilayer networks. In: IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2015)
Tieu, K., Dalley, G., Grimson, W.E.L.: Inference of non-overlapping camera network topology by measuring statistical dependence. In: Tenth IEEE International Conference on Computer Vision, 2005. ICCV 2005, vol. 2, pp. 1842–1849. IEEE (2005)
Acknowledgments
The authors would like to thank the DoD for partially sponsoring the current research. We would also like to thank and acknowledge the Naval Postgraduate School’s Center for Educational Design, Development, and Distribution (CED3) for creating the live visualization [4] for this project.
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Davis, B., Gera, R., Lazzaro, G., Lim, B.Y., Rye, E.C. (2016). The Marginal Benefit of Monitor Placement on Networks. In: Cherifi, H., Gonçalves, B., Menezes, R., Sinatra, R. (eds) Complex Networks VII. Studies in Computational Intelligence, vol 644. Springer, Cham. https://doi.org/10.1007/978-3-319-30569-1_7
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DOI: https://doi.org/10.1007/978-3-319-30569-1_7
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