Hostname: page-component-cd9895bd7-dk4vv Total loading time: 0 Render date: 2024-12-24T10:47:54.178Z Has data issue: false hasContentIssue false
  • English
  • Français

Stability of centrality measures in valued networks regarding different actor non-response treatments and macro-network structures

Published online by Cambridge University Press:  30 October 2017

ANJA ŽNIDARŠIČ ,
ANUŠKA FERLIGOJ  and
PATRICK DOREIAN
ANJA ŽNIDARŠIČ
Affiliation:
University of Maribor, Faculty of Organizational Sciences, Kidričeva 55a, 4000 Kranj, Slovenia (e-mail: anja.znidarsic@fov.uni-mb.si)
ANUŠKA FERLIGOJ
Affiliation:
University of Ljubljana, Faculty of Social Sciences, Kardeljeva ploščad 5, 1000 Ljubljana, Slovenia (e-mail: anuska.ferligoj@fdv.uni-lj.si)
PATRICK DOREIAN
Affiliation:
University of Ljubljana, Faculty of Social Sciences, Kardeljeva ploščad 5, 1000 Ljubljana, Slovenia University of Pittsburgh, Department of Sociology, USA (e-mail: pitpat@pitt.edu)
Get access Rights & Permissions [Opens in a new window]

Abstract

Social network data are prone to errors regardless their source. This paper focuses on missing data due to actor non-response in valued networks. If actors refuse to provide information, all values for outgoing ties are missing. Partially observed incoming ties to non-respondents and all other patterns for ties between members of the network can be used to impute missing outgoing ties. Many centrality measures are used to determine the most prominent actors inside the network. Using treatments for actor non-response enables us to estimate better the centrality scores of all actors regarding their popularity or prominence. Simulations using initial known blockmodel structures based on three most frequently occurring macro-network structures: cohesive subgroups, core-periphery models, and hierarchical structures were used to evaluate the relative merits of the treatments for non-response. The results indicate that the amount of non-respondents, the type of underlying macro-structure, and the employed treatment have an impact on centrality scores. Regardless of the underlying network structure, the median of the 3-nearest neighbors based on incoming ties performs the best. The adequacy (or not) of the other non-response treatments is contingent on the network macro-structure.

Keywords

valued networkactor non-responsemissing dataactor non-response treatmentsblockmodeling structureweighted centrality measures
Type
Research Article
Information
Network Science , Volume 6 , Issue 1 , March 2018 , pp. 1 - 33
Copyright
Copyright © Cambridge University Press 2017 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Beckfield, J. (2008). The dual world polity: Fragmentation and integration in the network of intergovernmental organizations. Social Problems, 55 (3), 419–442.Google Scholar
Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92 (5), 1170–1182.Google Scholar
Borgatti, S. P., Carley, K. M., & Krackhardt, D. (2006). On the robustness of centrality measures under conditions of imperfect data. Social Networks, 28 (2), 124–136.Google Scholar
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing social networks. London, UK: SAGE.Google Scholar
Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25 (2), 163–177.Google Scholar
Butts, C. T. (2015). sna 2.3-2: Tools for social network analysis. Retrieved from https://cran.r-project.org/web/packages/sna/index.html Google Scholar
Chinchilla-Rodríguez, Z., Ferligoj, A., Miguel, S., Kronegger, L., & de Moya-Anegón, F. (2012). Blockmodeling of co-authorship networks in library and information science in argentina: A case study. Scientometrics, 93 (3), 699–717.Google Scholar
Cohen, J. (1973). Eta-squared and partial eta-squared in fixed factor ANOVA designs. Educational and Psychological Measurement, 33 (1), 107–112.Google Scholar
Costenbader, E., & Valente, T. W. (2003). The stability of centrality measures when networks are sampled. Social Networks, 25 (4), 283–307.Google Scholar
Dijkstra, E. W. (1959). A note on two problems in connexion with graphs. Numerische Mathematik, 1, 269–271.Google Scholar
Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized Blockmodeling. New York, NY, USA: Cambridge University Press.Google Scholar
Ernstson, H., Sörlin, S., & Elmqvist, T. (2008). Social movements and ecosystem services: The role of social network structure in protecting and managing urban green areas in Stockholm. Ecology and Society, 13 (2), No. 39. Retrieved from http://www.ecologyandsociety.org/vol13/iss2/art39/ CrossRefGoogle Scholar
Frank, K. A. (1995). Identifying cohesive subgroups. Social Networks, 17 (1), 27–56.Google Scholar
Freeman, L. C. (1978). Centrality in social networks: Conceptual clarification. Social Networks, 1 (3), 215–239.Google Scholar
Giuliani, E. (2005). The structure of cluster knowledge networks: Uneven and selective, not pervasive and collective. In Frenken, K. (Ed.), Applied evolutionary economics and economic geography (pp. 5–11). Cheltenham: Edward Elgar Publishing Limited.Google Scholar
Guimerà, R., Danon, L., Díaz-Guilera, A., Giralt, F., & Arenas, A. (2003). Self-similar community structure in a network of human interactions. Physical Review E, 68 (065103).Google Scholar
Handcock, M. S., & Gile, K. J. (2010). Modeling social networks from sampled data. The Annals of Applied Statistics, 4 (1), 5–25.Google Scholar
Hipp, J. R., Wang, C., Butts, C. T., Jose, R., & Lakon, C. M. (2015). Research note: The consequences of different methods for handling missing network data in stochastic actor based models. Social Networks, 41, 56–71.Google Scholar
Huisman, M. (2009). Effects of missing data in social networks. Journal of Social Structure, 10, No. 1.Google Scholar
Huisman, M., & Steglich, C. (2008). Treatment of non-response in longitudinal network studies. Social Networks, 30 (4), 297–308.Google Scholar
Kolli, N, & Narayanaswamy, B. (2013). Analysis of e-mail communication using a social network framework for crisis detection in an organization. Procedia – Social and Behavioral Sciences, 100, 57–671.Google Scholar
Kossinets, G. (2006). Effects of missing data in social networks. Social Networks, 28 (3), 247–268.CrossRefGoogle Scholar
Kronegger, L., Ferligoj, A., & Doreian, P. (2011). On the dynamics of national scientific systems. Quality & Quantity, 45 (5), 989–1015.Google Scholar
Levine, T. R., & Hullett, C. R. (2002). Eta squared, partial eta squared, and misreporting of effect size in communication research. Human Communication Research, 28 (4), 612–625.Google Scholar
Markantonatou, V., Noguera-Méndez, P., Semitiel-García, M., Hogg, K., & Sano, M. (2016). Social networks and information flow: Building the ground for collaborative marine conservation planning in Portofino Marine Protected Area (MPA). Ocean and Coastal Management, 120, 29–38.CrossRefGoogle Scholar
Marsden, P. V. (2005). Recent developments in network measurement. In Carrington, P. J., Scott, J., & Wasserman, S. (Eds.), Models and methods in social network analysis (pp. 8–30). New York, NY: Cambridge University Press.Google Scholar
Memon, N., Larsen, H. L., Hicks, D. L., & Harkiolakis, N. (2008). Detecting hidden hierarchy in terrorist networks: Some case studies. In Yang, C. C., Chen, H., Chau, M., Chang, K., Lang, S., Chen, P. S., Hsieh, R., Zeng, D., Wang, F.-Y., Carley, K., Mao, W., & Zhan, J., (Eds.), Intelligence and security informatics. Lecture notes in computer science, vol. 5075 (pp. 477–489). Berlin, Heidelberg: Springer.Google Scholar
Newman, M. E. J. (2001). Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality. Physical Review E, 64, 016132.Google Scholar
Niu, Q., Zeng, A., Fan, Y., & Di, Z. (2015). Robustness of centrality measures against network manipulation. Physica A: Statistical Mechanics and its Applications, 438, 124–131.Google Scholar
Opsahl, T. (2012). tnet 3.0.11: Software for analysis of weighted, two-mode, and longitudinal networks. Retrieved from https://CRAN.R-project.org/package=tnet Google Scholar
Opsahl, T., Agneessens, F., & Skvoretz, J. (2010). Node centrality in weighted networks: Generalizing degree and shortest paths. Social Networks, 32, 245–251.Google Scholar
Páez, A., Scott, D. M., & Volz, E. (2008). Weight matrices for social influence analysis: An investigation of measurement errors and their effect on model identification and estimation quality. Social Networks, 30 (4), 309–317.Google Scholar
Potter, G. E., Smieszek, T., & Sailer, K. (2015). Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions. Network Science, 3 (9), 298–325.Google Scholar
Rašković, M., Udovič, B., & Žnidaršič, A. (2015). Network analysis of inter-country export patterns in the EU: Implications for small states. Teorija in Praksa, LII (1–2), 150–174.Google Scholar
Robins, G., Pattison, P., & Woolcock, J. (2004). Missing data in networks: exponential random graph (p*) models for networks with non-respondents. Social Networks, 26 (3), 257–283.Google Scholar
Rumsey, D. J. (1993). Nonresponse models for social network stochastic processes. Ph.D. thesis, The Ohio State University.Google Scholar
Scott, J. (2013). Social network analysis (3rd ed.). London, UK: SAGE.Google Scholar
Smith, J. A., & Moody, J. (2013). Structural effects of network sampling coverage I: Nodes missing at random. Social Networks, 35 (4), 652–668.Google Scholar
Squartini, T., Picciolo, F., Ruzzenenti, F., & Garlaschelli, D. (2013). Reciprocity of weighted networks. Scientific Reports, 3, No. 2729.Google Scholar
Stork, D., & Richards, W. D. (1992). Nonrespondents in communication network studies: problems and possibilities. Group and Organization Management, 17, 193–209.Google Scholar
Wasserman, S., & Faust, K. (1998). Social network analysis: Methods and applications (2nd ed.). Cambridge, USA: Cambridge University Press.Google Scholar
Žiberna, A. (2009). Evaluation of direct and indirect blockmodeling of regular equivalence in valued networks by simulations. Metodološki Zvezki, 6, 99–134.Google Scholar
Žiberna, A. (2010). Blockmodeling 0.1.8: An R package for generalized and classical blockmodeling of valued networks. Retrieved from https://cran.r-project.org/web/packages/blockmodeling/index.html Google Scholar
Žnidaršič, A., Doreian, P., & Ferligoj, A. (2012a). Absent ties in social networks, their treatments, and blockmodeling outcomes. Metodološki Zvezki, 9, 119–138.Google Scholar
Žnidaršič, A., Ferligoj, A., & Doreian, P. (2012b). Non-response in social networks: The impact of different non-response treatments on the stability of blockmodels. Social Networks, 34, 438–450.Google Scholar
Žnidaršič, A., Ferligoj, A., & Doreian, P. (2017). Actor non-response in valued social networks: The impact of different non-response treatments on the stability of blockmodels. Social Networks, 48, 46–56.Google Scholar