Network collection, sampling and comparison

Hidden populations and network-based sampling. Network comparison by fragments, distances and metrics. Network backbones and skeletons.

Lecture handouts:

• (12) Network collection and sampling
• (handout) Overview of network sampling
• (12) Network similarity and comparison
• (handout) Network comparison methods
• (12) Network backbones and skeletons

Lab handouts:

• (xii) Random-walk sampling and Facebook (I, II)
• (xii) Networks and models comparison (III)

Book chapters:

• → Ch. 3.7: Snowball sampling etc.  in Newman, M.E.J., Networks: An Introduction (Oxford University Press, 2010).
  

Course readings:

• Granovetter, M., Network sampling: Some first steps, Am. J. Sociol. 81(6), 1287–1303 (1976).
  
• → Song, C., Havlin, S. & Makse, H.A., Self-similarity of complex networks, Nature 433(7024), 392-395 (2005).
• Lee, S.H., Kim, P.-J. & Jeong, H., Statistical properties of sampled networks,  Phys. Rev. E 73(1), 016102 (2006).
• → Leskovec, J. & Faloutsos, C., Sampling from large graphs, In: Proceedings of KDD '06 (Philadelphia, PA, USA, 2006), pp. 631-636.
• Furuya, S. & Yakubo, K., Multifractality of complex networks, Phys. Rev. E 84(3), 036118 (2011).
• Laurienti, P.J., Joyce, K.E. et al., Universal fractal scaling of self-organized networks, Physica A 390(20), 3608–3613 (2011).
• Blagus, N., Šubelj, L. & Bajec, M., Self-similar scaling of density in complex real-world networks, Physica A 391(8), 2794-2802 (2012).
• Guimerà, R., Danon, L. et al., Self-similar community structure in a network of human interactions, Phys. Rev. E 68(6), 065103 (2003).
• Blagus, N., Šubelj, L. et al., Sampling promotes community structure in social and information networks, Physica A 432, 206-215 (2015).
• Blagus, N., Šubelj, L. & Bajec, M., Assessing the effectiveness of real-world network simplification,  Physica A 413, 134-146 (2014).
• → Blagus, N., Šubelj, L. & Bajec, M., Empirical comparison of network sampling, Physica A 477, 136–148 (2017).

• → Milo, R., Itzkovitz, S. et al., Superfamilies of evolved and designed networks, Science 303(5663), 1538-1542 (2004).
• → Pržulj, N., Biological network comparison using graphlet degree distribution, Bioinformatics 23(2), e177-e183 (2007).
• Cooper, K. & Barahona, M., Role-similarity based comparison of directed networks, e-print arXiv:1103.5582v1, pp. 6 (2011).
• Yaveroğlu, Ö.N., Malod-Dognin, N. et al., Revealing the hidden language of complex networks, Sci. Rep. 4, 4547 (2014).
• Aparício, D., Ribeiro, P. & Silva, F., Network comparison using directed graphlets, e-print arXiv:1511.01964v1, pp. 9 (2015).
• Schieber, T.A., Carpi, L. et al., Quantification of network structural dissimilarities, Nat. Commun. 8, 13928 (2017).
  
• → Bagrow, J.P. & Bollt, E.M., An information-theoretic, all-scales approach to comparing networks, e-print arXiv:1804.03665v1, pp. 18 (2018).
• → Šubelj, L., Fiala, D. & Bajec, M., Network-based statistical comparison of bibliographic databases , Sci. Rep. 4, 6496 (2014).
• Šubelj, L., Bajec, M. et al., Quantifying the consistency of scientific databases, PLoS ONE 10(5), e0127390 (2015).
• Demšar, J., Statistical comparisons of classifiers over multiple data sets, J. Mach. Learn. Res. 7, 1–30 (2006).

• → Grady, D., Thiemann, C. & Brockmann, D., Robust classification of salient links in complex networks, Nat. Commun. 3, 864 (2012).
  
• → van den Heuvel, M.P., Kahn, R.S. et al., High-cost, high-capacity backbone for global brain communication, P. Natl. Acad. Sci. USA 109(28), 11372–11377 (2012).
  
• Ruan, N., Jin, R. et al., Network backbone discovery using edge clustering, e-print arXiv:1202.1842v2, pp. 14 (2012). 
  
• Hamann, M., Lindner, G. et al., Structure-preserving sparsification methods for social networks, Soc. Netw. Anal. Min. 6(1), 22 (2016).
  
• Coscia, M. & Neffke, F., Network backboning with noisy data, In: Proceedings of ICDE '17 (San Diego, CA, USA, 2017), pp. 425–436.
  
• → Šubelj, L., Convex skeletons of complex networks, J. R. Soc. Interface 15(145), 20180422 (2018).