Abstract
With increasing digitization a wide variety of systems from diverse domains such as computer science, transportation, social science have become available in the form of networks. It is argued that to understand complex systems a deep understanding of the networks behind them is needed. A network theoretic perspective provides valuable insights into the structure and trends of systems. Data-sets belonging to different domains have their own unique features and behavioural trends and the current inquiry aims to highlight this. In this inquiry, a comprehensive analysis of synthetic and real-world published benchmark data-sets, evaluation methods, and open source projects is performed. The aim is to provide novice and expert users with tools for algorithmic designs and methodologies. Empirical studies are used to compare the performance of network theoretic tools on common data-sets. Finally, limitations of the network perspective on systems are listed and research directions to facilitate future study are elaborated.
Similar content being viewed by others
References
Handcock MS, Gile KJ (2010) Modeling social networks from sampled data. Ann Appl Stat 4(1):5
Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44
Gomez V, Kaltenbrunner A, Lopez V (2008) Statistical analysis of the social network and discussion threads in slashdot. In: Proceedings of the 17th international conference on World Wide Web. ACM, New York, pp 645–654
Barabási A-L et al (2016) Network science. Cambridge University Press, Cambridge
Page L, Brin S, Motwani R, Winograd T (1998) The PageRank citation ranking: bringing order to the web. In: Proceedings of the 7th international world wide web conference, Brisbane, Australia, pp 161–172. https://www.bibsonomy.org/bibtex/2ac49c33e114ca171db40cece6a0ae4d6/sac
Brin S, Page L (1998) The anatomy of a large-scale hypertextual web search engine. Comput Netw ISDN Syst 30(1–7):107–117
McGlohon M, Akoglu L, Faloutsos C (2011) Statistical properties of social networks. In: Social network data analytics. Springer, New York, pp 17–42
Hoff PD, Raftery AE, Handcock MS (2002) Latent space approaches to social network analysis. J Lashdot Stat Assoc 97(460):1090–1098
Jackson MO (2010) Social and economic networks. Princeton University Press, Princeton
Shi Y, Gui H, Zhu Q, Kaplan L, Han J (2018) Aspem: embedding learning by aspects in heterogeneous information networks. In: Proceedings of the 2018 SIAM International Conference on Data Mining. SIAM, pp 144–152
Leskovec J, Kleinberg J, Faloutsos C (2007) Graph evolution: densification and shrinking diameters. ACM Trans Knowl Discov Data 1(1):2–49
Granell C, Darst RK, Arenas A, Fortunato S, Gómez S (2015) Benchmark model to assess community structure in evolving networks. Phys Rev E 92(1):12–19
Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442
Erdos P, Rényi A (1960) On the evolution of random graphs. Publ Math Inst Hung Acad Sci 5(1):17–60
Ricci V (2005) Fitting distributions with R. https://www.bibsonomy.org/bibtex/289d38b1135b797469c33d514a7677ff2/folke
Johnson, RA, Wichern, DW et al. (2002) Applied multivariate statistical analysis, vol 5. Prentice Hall, Upper Saddle River, NJ
Tan W, Brian Blake M, Saleh I, Dustdar S (2013) Social-network-sourced big data analytics. IEEE Int Comput 17(5):62–69
Friedman BD, Burns MJ, Cao J (2014) Enterprise social networking data analytics within Alcatel-Lucent. Bell Labs Tech J 18(4):89–109
Gewerc A, Marteiro E (2016) Academic social networks and learning analytics to explore self-regulated learning: a case study. IEEE Rev Iberoam de Tecnol del Aprendiz 11(3):159–166
Dwyer C, Hiltz S, Passerini K (2007) Trust and privacy concern within social networking sites: A comparison of facebook and myspace. AMCIS 2007 proceedings, pp 324–339
Lancichinetti A, Fortunato S (2009) Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys Rev E 80(1):118–129
Leskovec J, Krevl A (2014) SNAP datasets: stanford large network dataset collection. http://snap.stanford.edu/data
Dua D, Taniskidou K (2017) UCI: machine learning repository. http://archive.ics.uci.edu/ml
Mislove A, Marcon M, Gummadi KP, Druschel P, Bhattacharjee B (2007) Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM conference on Internet measurement. ACM, New York, pp 29–42
Leskovec J, Lang KJ, Dasgupta A, Mahoney MW (2009) Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math 6(1):29–123
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Nerurkar, P., Chandane, M. & Bhirud, S. Empirical analysis of synthetic and real networks. Int. j. inf. tecnol. 14, 1061–1073 (2022). https://doi.org/10.1007/s41870-019-00344-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s41870-019-00344-4