Papers
Topics
Authors
Recent
Search
2000 character limit reached

When can networks be inferred from observed groups?

Published 24 Jul 2023 in cs.SI | (2307.12833v3)

Abstract: Collecting network data directly from network members can be challenging. One alternative involves inferring a network from observed groups, for example, inferring a network of scientific collaboration from researchers' observed paper authorships. In this paper, I explore when an unobserved undirected network of interest can accurately be inferred from observed groups. The analysis uses simulations to experimentally manipulate the structure of the unobserved network to be inferred, the number of groups observed, the extent to which the observed groups correspond to cliques in the unobserved network, and the method used to draw inferences. I find that when a small number of groups are observed, an unobserved network can be accurately inferred using a simple unweighted two-mode projection, provided that each group's membership closely corresponds to a clique in the unobserved network. In contrast, when a large number of groups are observed, an unobserved network can be accurately inferred using a statistical backbone extraction model, even if the groups' memberships are mostly random. These findings offer guidance for researchers seeking to indirectly measure a network of interest using observations of groups.

Authors (1)
Definition Search Book Streamline Icon: https://streamlinehq.com
References (43)
  1. j. adams. Gathering social network data. Sage, 2020. https://doi.org/10.4135/9781544321486.
  2. The rise of partisanship and super-cooperators in the us house of representatives. PLOS One, 10(4):e0123507, 2015. https://doi.org/10.1371/journal.pone.0123507.
  3. A.-L. Barabási and R. Albert. Emergence of scaling in random networks. Science, 286(5439):509–512, 1999.
  4. Comparing fields of sciences: multilevel networks of research collaborations in italian academia. Multilevel network analysis for the social sciences: Theory, methods and applications, pages 213–244, 2016.
  5. Models of core/periphery structures. Social Networks, 21(4):375–395, 2000. https://doi.org/10.1016/S0378-8733(99)00019-2.
  6. Analyzing affiliation networks. The Sage Handbook of Social Network Analysis, 1:417–433, 2011.
  7. R. L. Breiger. The duality of persons and groups. Social Forces, 53(2):181–190, 1974. https://doi.org/10.1093/sf/53.2.181.
  8. J. Brennecke and O. N. Rank. Knowledge networks in high-tech clusters: A multilevel perspective on interpersonal and inter-organizational collaboration. Multilevel network analysis for the social sciences: Theory, methods and applications, pages 273–293, 2016.
  9. D. Chicco and G. Jurman. The advantages of the matthews correlation coefficient (mcc) over f1 score and accuracy in binary classification evaluation. BMC Genomics, 21:1–13, 2020. https://doi.org/10.1186/s12864-019-6413-7.
  10. P. Erdős and A. Rényi. On random graphs. Publicationes Mathematicae, 6:290–297, 1959.
  11. S. L. Feld. The focused organization of social ties. American Journal of Sociology, 86(5):1015–1035, 1981. https://doi.org/10.1086/227352.
  12. On network backbone extraction for modeling online collective behavior. PLOS One, 17(9):e0274218, 2022. https://doi.org/10.1371/journal.pone.0274218.
  13. K. Godard and Z. P. Neal. fastball: A fast algorithm to sample binary matrices with fixed marginals. Journal of Complex Networks, 10(6):cnac049, 2022. https://doi.org/10.1093/comnet/cnac047.
  14. J.-L. Guillaume and M. Latapy. Bipartite structure of all complex networks. Information Processing Letters, 90(5):215–221, 2004. https://doi.org/10.1016/j.ipl.2004.03.007.
  15. Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722):697–702, 2005. https://doi.org/10.1126/science.1106340.
  16. B. Kapferer. Strategy and transaction in an African factory: African workers and Indian management in a Zambian town. Manchester University Press, 1972.
  17. R. M. Karp. Reducibility among combinatorial problems. In R. E. Miller, J. W. Thatcher, and J. D. Bohlinger, editors, Complexity of Computer Computations, pages 85–103. Springer US, Boston, MA, 1972. ISBN 978-1-4684-2001-2. https://doi.org/10.1007/978-1-4684-2001-2_9. URL https://doi.org/10.1007/978-1-4684-2001-2_9.
  18. G. Kossinets. Effects of missing data in social networks. Social Networks, 28(3):247–268, 2006. https://doi.org/10.1016/j.socnet.2005.07.002.
  19. Animal social networks: an introduction. Behavioral Ecology and Sociobiology, 63:967–973, 2009. https://doi.org/10.1007/s00265-009-0747-0.
  20. Basic notions for the analysis of large two-mode networks. Social Networks, 30(1):31–48, 2008. https://doi.org/10.1016/j.socnet.2007.04.006.
  21. E. Lazega. The collegial phenomenon: The social mechanisms of cooperation among peers in a corporate law partnership. Oxford University Press, USA, 2001.
  22. The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations: can geographic isolation explain this unique trait? Behavioral Ecology and Sociobiology, 54:396–405, 2003. https://doi.org/10.1007/s00265-003-0651-y.
  23. P. V. Marsden. Survey methods for network data. In J. Scott and P. J. Carrington, editors, The Sage Handbook of Social Network Analysis, pages 370–388. 2011.
  24. M. S. Mizruchi. What do interlocks do? an analysis, critique, and assessment of research on interlocking directorates. Annual Review of Sociology, 22(1):271–298, 1996. https://doi.org/10.1146/annurev.soc.22.1.271.
  25. J. W. Neal. A systematic review of social network methods in high impact developmental psychology journals. Social Development, 29(4):923–944, 2020a. https://doi.org/10.1111/sode.12442.
  26. Inferring signed networks from preschoolers’ observed parallel and social play. Social Networks, 77, 2022. https://doi.org/10.1016/j.socnet.2022.07.002.
  27. Z. Neal. Structural determinism in the interlocking world city network. Geographical Analysis, 44(2):162–170, 2012. https://doi.org/10.1111/j.1538-4632.2012.00843.x.
  28. Z. P. Neal. The backbone of bipartite projections: Inferring relationships from co-authorship, co-sponsorship, co-attendance and other co-behaviors. Social Networks, 39:84–97, 2014. https://doi.org/10.1016/j.socnet.2014.06.001.
  29. Z. P. Neal. A sign of the times? weak and strong polarization in the us congress, 1973–2016. Social Networks, 60:103–112, 2020b. https://doi.org/10.1016/j.socnet.2018.07.007.
  30. Z. P. Neal. Constructing legislative networks in r using incidentally and backbone. Connections, 42:1–9, 2022. https://doi.org/10.2478/connections-2019.026.
  31. Z. P. Neal. The duality of networks and groups: Models to generate two-mode networks from one-mode networks. Network Science, pages 1–14, 2023. https://doi.org/10.1017/nws.2023.3.
  32. Comparing alternatives to the fixed degree sequence model for extracting the backbone of bipartite projections. Scientific Reports, 11:23929, 2021. https://doi.org/10.1038/s41598-021-03238-3.
  33. M. E. Newman. Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1):5200–5205, 2004. https://doi.org/10.1073/pnas.0307545100.
  34. Robust action and the rise of the medici, 1400-1434. American Journal of Sociology, 98(6):1259–1319, 1993. https://doi.org/10.1086/230190.
  35. Statistical inference links data and theory in network science. Nature Communications, 13(1):6794, 2022. https://doi.org/10.1038/s41467-022-34267-9.
  36. Fundamental principles of network formation among preschool children. Social Networks, 32(1):61–71, 2010. https://doi.org/10.1016/j.socnet.2009.04.003.
  37. How do youth choose activities? assessing the relative importance of the micro-selection mechanisms behind adolescent extracurricular activity participation. Social Networks, 2022. https://doi.org/10.1016/j.socnet.2021.12.008.
  38. Measurement error in network data: A re-classification. Social Networks, 34(4):396–409, 2012. https://doi.org/10.1016/j.socnet.2012.01.003.
  39. D. J. Watts. Networks, dynamics, and the small-world phenomenon. American Journal of Sociology, 105(2):493–527, 1999. https://doi.org/10.1086/210318.
  40. D. J. Watts. Six Degrees: The Science of a Connected Age. W. W. Norton, 2008.
  41. Collective dynamics of ‘small-world’networks. Nature, 393(6684):440–442, 1998. https://doi.org/10.1038/30918.
  42. B. Wellman. Structural analysis: From method and metaphor to theory and substance. In B. Wellman and S. D. Berkowitz, editors, Social structures: A network approach, pages 19–61. Cambridge University Press, 1988.
  43. W. W. Zachary. An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4):452–473, 1977. https://doi.org/10.1086/jar.33.4.3629752.
Citations (1)
View on Semantic Scholar

Summary

No one has generated a summary of this paper yet.

Sign Up to Summarize

Paper to Video (Beta)

No one has generated a video about this paper yet.

Sign Up to Generate All Videos Subscribe on YouTube

Whiteboard

No one has generated a whiteboard explanation for this paper yet.

Sign Up to Generate

Open Problems

We haven't generated a list of open problems mentioned in this paper yet.

Generate Now

Continue Learning

We haven't generated follow-up questions for this paper yet.

Generate Now

Collections

Sign up for free to add this paper to one or more collections.

Sign Up