Abstract
Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks can be generated by formal rules. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how models can be fitted to an original network to produce a structurally similar replica, and (c) aim for producing much larger networks than the original exemplar. In a comparative experimental study, we find ReCoN often superior to many other stateof- the-art network generation methods. Our design yields a scalable and effective tool for replicating a given network while preserving important properties at both microand macroscopic scales and (optionally) scaling the replica by orders of magnitude in size. We recommend ReCoN as a general practical method for creating realistic test data for the engineering of computational methods on networks, verification, and simulation studies. We provide scalable open-source implementations of most studied methods, including ReCoN.
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References
Aiello, W., Chung, F., Lu, L.: A random graph model for massive graphs. In: Proceedings of the thirty-second annual ACM symposium on Theory of computing, pp. 171–180. Acm (2000)
Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of modern physics 74(1), 47 (2002)
An, W.: Fitting ERGMs on big networks. Social Science Research 59, 107 – 119 (2016). Special issue on Big Data in the Social Sciences
Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics reports 424(4), 175–308 (2006)
Caldarelli, G., Vespignani, A.: Large scale structure and dynamics of complex networks. World Scientific (2007)
Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: A recursive model for graph mining. In: Proc. 4th SIAM Intl. Conf. on Data Mining (SDM). SIAM, Orlando, FL (2004)
Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: A recursive model for graph mining. Computer Science Department p. 541 (2004)
Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM review 51(4), 661–703 (2009)
Dasari, N.S., Ranjan, D., Zubair, M.: ParK: An efficient algorithm for k-core decomposition on multicore processors. In: 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014, pp. 9–16. IEEE (2014)
Fortunato, S.: Benchmark graphs to test community detection algorithms. URL https: //sites.google.com/site/santofortunato/inthepress2
Geisberger, R., Sanders, P., Schultes, D.: Better approximation of betweenness centrality. In: ALENEX, pp. 90–100. SIAM (2008)
Goldenberg, A., Zheng, A.X., Fienberg, S.E., Airoldi, E.M.: A survey of statistical network models. Foundations and TrendsR in Machine Learning 2(2), 129–233 (2010)
Gutfraind, A., Meyers, L., Safro, I.: Musketeer: Multiscale entropic network generator. URL https://people.cs.clemson.edu/˜isafro/musketeer/index.html
Gutfraind, A., Safro, I., Meyers, L.A.: Multiscale network generation. In: 18th International Conference on Information Fusion, FUSION 2015, Washington, DC, USA, July 6-9, 2015, pp. 158–165 (2015)
Hamann, M., Lindner, G., Meyerhenke, H., Staudt, C.L., Wagner, D.: Structure-preserving sparsification methods for social networks. Social Netw. Analys. Mining 6(1), 22:1–22:22 (2016)
Kolda, T.G., Pinar, A., Plantenga, T., Seshadhri, C.: A scalable generative graph model with community structure. arXiv preprint arXiv:1302.6636 (2013)
Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguñá, M.: Hyperbolic geometry of complex networks. Physical Review E 82, 036,106 (2010)
Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80(1), 016,118 (2009)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Physical Review E 78(4), 046,110 (2008)
Leskovec, J., Faloutsos, C.: Scalable modeling of real graphs using kronecker multiplication. In: Proc. 24th Intl. Conference on Machine learning, pp. 497–504. ACM (2007)
von Looz, M., Meyerhenke, H., Prutkin, R.: Generating random hyperbolic graphs in subquadratic time. In: Algorithms and Computation - 26th International Symposium, ISAAC 2015, Nagoya, Japan, December 9-11, 2015, Proceedings, pp. 467–478 (2015)
Milo, R., Kashtan, N., Itzkovitz, S., Newman, M.E.J., Alon, U.: On the uniform generation of random graphs with prescribed degree sequences. eprint arXiv:cond-mat/0312028 (2003)
Newman, M.: Networks: an introduction. Oxford University Press (2010)
P. Erdős, A.R.: On the Evolution of Random Graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences (1960)
Robins, G., Pattison, P., Kalish, Y., Lusher, D.: An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2), 173 – 191 (2007). Special Section: Advances in Exponential Random Graph (p*) Models
Schlauch, W.E., Horvát, E.Á ., Zweig, K.A.: Different flavors of randomness: comparing random graph models with fixed degree sequences. Social Network Analysis and Mining 5(1), 1–14 (2015). DOI 10.1007/s13278-015-0267-z
Snijders, T.A.: The statistical evaluation of social network dynamics. Sociological methodology 31(1), 361–395 (2001)
Staudt, C., Hamann, M., Safro, I., Gutfraind, A., Meyerhenke, H.: Generating Scaled Replicas of Real-World Complex Networks. Tech. rep., arXiv (2016). URL http://arxiv.org/abs/1609.02121. ArXiv:1609.02121
Staudt, C.L.: Algorithms and software for the analysis of large complex networks. Ph.D. thesis, Karlsruhe Institute of Technology (2016). DOI 10.5445/IR/1000056470
Staudt, C.L., Meyerhenke, H.: Engineering parallel algorithms for community detection in massive networks. IEEE Trans. on Parallel and Distributed Systems 27(1), 171–184 (2016)
Staudt, C.L., Sazonovs, A., Meyerhenke, H.: NetworKit: A tool suite for large-scale network analysis. Network Science To appear
Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of facebook networks. Physica A: Statistical Mechanics and its Applications 391(16), 4165–4180 (2012)
Viger, F., Latapy, M.: Random generation of large connected simple graphs with prescribed degree distribution. In: 11th International Conference on Computing and Combinatorics. Kunming, Yunnan, Chine (2005)
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Staudt, C.L., Hamann, M., Safro, I., Gutfraind, A., Meyerhenke, H. (2017). Generating Scaled Replicas of Real-World Complex Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_2
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