Two-mode networks are used to describe dual patterns of association between distinct social entities through their joint involvement in categories, activities, issues, and events. In empirical organizational research, the analysis of two-mode networks is typically accomplished either by (a) decomposition of the dual structure into its two unimodal components defined in terms of indirect relations between entities of the same kind or (b) direct statistical analysis of individual two-mode dyads. Both strategies are useful, but neither is fully satisfactory. In this article, the authors introduce newly developed stochastic actor-based models for two-mode networks that may be adopted to redress the limitations of current analytical strategies. The authors specify and estimate the model in the context of data they have collected on the dual association between software developers and software problems observed during a complete release cycle of an open source software project. The authors discuss the general methodological implications of the models for organizational research based on the empirical analysis of two-mode networks.
AhujaG.SodaG.ZaheerA. (2011). The genesis and dynamics of organizational networks. Organization Science. Advance online publication.
2.
ArabieP. J.CarrollD.DeSarboW. S. (1987). Three-way scaling and clustering. Newbury Park, CA: Sage.
3.
ArgoteL.EppleD. (1990). Learning curves in manufacturing. Science, 247(4945), 920–924.
4.
BirdC.GourleyA.DevanbuP.GertzM.SwaminathanA. (2006). Mining email social networks. In Proceedings of the 2006 International Workshop on Mining Software Repositories (pp. 137–143). Retrieved fromhttp://msr.uwaterloo.ca/msr2006/MSR-06-proceedings.pdf.
5.
BorgattiS.EverettM. (1997). Network analysis of 2-mode data. Social Networks, 19(3), 243–269.
6.
BowmanK. O.ShentonL. R. (1985). Method of moments. In KotzS.JohnsonN. L.ReadC. B. (Eds.). Encyclopedia of statistical sciences (pp. 467–473). New York, NY: John Wiley.
7.
BreigerR. (1974). The duality of persons and groups. Social Forces, 53(2), 181–190.
8.
BreigerR. (2000). A toolkit for practice theory. Poetics, 27(2-3), 91–115.
9.
BreigerR. (2009). On the duality of cases and variables: Correspondence analysis (CA) and qualitative comparative analysis (QCA). In ByrneD.RaginC. C. (Eds.), The SAGE handbook of case-based methods (pp. 243–259). London: Sage.
10.
BreigerR. L.MohrJ. W. (2004). Institutional logics from the aggregation of organizational networks: Operational procedures for the analysis of counted data. Computational & Mathematical Organization Theory, 10(1), 17–43.
11.
BreschiS.LissoniF. (2005). Cross–firm inventors and social networks: Localised knowledge spillovers revisited. Annales d’Economie et de Statistique, 79-80, 189–209.
12.
BurtR.TalmudI. (1993). Market niche. Social Networks, 15, 133–149.
13.
CarleyK. M. (1991). A theory of group stability. American Sociological Review, 56(3), 331–354.
CrowstonK.HowisonJ. (2006). Hierarchy and centralization in free and open source software team communications. Knowledge Technology & Policy, 18, 65–85.
16.
CrowstonK.ScozziB. (2008). Bug fixing practices within free/libre open source software development teams. Journal of Database Management, 19(2), 1–30.
17.
DavidP. A.RullaniF. (2008). Dynamics of innovation in an “open source” collaboration environment: Lurking, laboring and launching FLOSS projects on SourceForge. Industrial and Corporate Change, 17(4), 647–710.
18.
DoreianP.BatageljV.FerligojA. (2004). Generalized blockmodeling of two-mode network data. Social Networks, 26(1), 29–53.
19.
FaustK. (2005). Using correspondence analysis for joint displays of affiliation networks. In CarringtonP.ScottJ.WassermanS. (Eds.), Models and methods in social network analysis (pp. 117–147). New York, NY: Cambridge University Press.
20.
FeldS. L. (1981). The focused organization of social ties. American Journal of Sociology, 86(5), 1015–1035.
21.
FordD. N.StermanJ. D. (2003). Overcoming the 90% syndrome: Iteration management in concurrent development projects. Concurrent Engineering, 11, 177–186.
22.
FreemanL.WhiteD. R. (1993). Using Galois lattices to represent network data. Sociological Methodology, 23, 127–146.
23.
GermanD. M. (2004). The GNOME project: A case study of open source, global software development. Software Process: Improvement and Practice, 8(4), 201–215.
GreeneW. H. (2003). Econometrics analysis. Upper Saddle River, NJ: Prentice Hall.
26.
HallenB. L. (2008). The causes and consequences of the initial network positions of new organizations: From whom do entrepreneurs receive investments?Administrative Science Quarterly, 53(4), 685–718.
27.
HollandP. W.LeinhardtS. (1977). A dynamic model for social networks. Journal of Mathematical Sociology, 5, 5–20.
28.
HunterD. R.GoodreauS. M.HandcockM. S. (2008). Goodness of fit of social network models. Journal of the American Statistical Association, 103(481), 248–258.
29.
KoskinenJ.EdlingC. (in press). Modelling the evolution of a bipartite network: Peer referral in interlocking directorates. Social Networks.
LernerJ.TiroleJ. (2001). The open source movement: Key research questions. European Economic Review, 45(4-6), 819–826.
32.
LomiA.LarsenE. R. (2001). Dynamics of organizations: Computational modeling and organization theories. Cambridge, MA: MIT Press.
33.
LomiA.SnijdersT. A. B.SteglichC. E. G.TorlòV. J. (2011). Why are some more peer than others? Evidence from a longitudinal study of social networks and individual academic performance. Social Science Research, 40, 1506–1520.
34.
LospinosoJ. A.SchweinbergerM.SnijdersT. A. B.RipleyR. M. (2010). Assessing and accounting for time heterogeneity in stochastic actor oriented models. Advances in Data Analysis and Classification, 5, 147–176.
35.
LunguM.MalnatiJ.LanzaM. (2009).Visualizing GNOME with the small project observatory. In Proceedings of the 6th International Working Conference on Mining Software Repositories (pp. 103–106). Retrieved fromhttp://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=5040630.
36.
MacyM. W.WillerR. (2002). From factors to actors: Computational sociology and agent-based modeling. Annual Review of Sociology, 28, 143–66.
37.
MaddalaG. S. (1983). Limited-dependent and qualitative variables in econometrics. Cambridge, UK: Cambridge University Press.
38.
ManskiC. F.McFaddenD. (1981). Alternative estimators and sample designs for discrete choice analysis. In ManskiC. F.McFaddenD. (Eds.), Structural analysis of discrete data with econometric applications (pp. 2–50). Cambridge, MA: MIT Press.
39.
MischeA.PattisonP. (2000). Composing a civic arena: Publics, projects, and social settings. Poetics, 27(2-3), 163–194.
40.
MockusA.FieldingR. T.HerbslebJ. D. (2002). Two case studies of open source software development: Apache and Mozilla. ACM Transactions on Software Engineering and Methodology (TOSEM), 11(3).
41.
MohrJ. W. (1998). Measuring meaning structures. Annual Review of Sociology, 24, 345–370.
42.
MohrJ. W.DuquenneV. (1997). The duality of culture and practice: Poverty relief in New York City, 1888-1917. Theory and Society, 26, 305–356.
43.
MohrJ. W.Guerra-PearsonF. (2010). The duality of niche and form: The differentiation of institutional space in New York City, 1888-1917. In HsuG.KocakO.NegroG. (Eds.), Categories in markets: Origins and evolution, research in the sociology of organizations (pp. 321–368). Bingley: Emerald Press.
44.
NewmanM. E. J. (2001). The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences of the United States of America, 98(2), 404–409.
45.
NewmanM. E. J.StrogatzS. H.WattsD. J. (2001). Random graphs with arbitrary degree distributions and their applications. Physical Review E, 64. Retrieved fromhttp://pre.aps.org/abstract/PRE/v64/i2/e026118.
O’MahonyS. (2003). Guarding the commons: How community managed software projects protect their work. Research Policy, 32(7), 1179–1198.
48.
O’MahonyS.FerraroF. (2007). The emergence of governance in an open source community. Academy of Management Journal, 50(5), 1079–1106.
49.
PattisonP. E.BreigerR. L. (2002). Lattices and dimensional representations: Matrix decompositions and ordering structures. Social Networks, 24, 423–444.
50.
PattisonP. E.RobinsG. (2002). Neighborhood-based models for social networks. Sociological Methodology, 32, 301–337.
51.
PodolnyJ. M. (1994). Market uncertainty and the social character of economic exchange. Administrative Science Quarterly, 39(3), 458–483.
52.
PodolnyJ. M.StuartT.HannanM. T. (1996). Networks, knowledge, and niches: Competition in the worldwide semiconductor industry, 1984-1991. American Journal of Sociology, 102(3), 659–689.
53.
QureshiI.FangY. (2011). Socialization in open source software projects: A growth mixture modeling approach. Organizational Research Methods, 14(1), 208–238.
54.
R Development Core Team (2011). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.
RoblesG.González-BarahonaJ. M.Izquierdo-CortazarD.HerraizI. (2009). Tools for the study of the usual data sources found in Libre Software projects. International Journal of Open Source Software & Processes, 1(1), 24–45.
62.
SaavedraS.Reed-TsochasF.UzziB. (2009). A simple model of bipartite cooperation for ecological and organizational networks. Nature, 457, 463–466.
63.
ScacchiW. (2002). Understanding the requirements for developing open source software systems. IEEE Software, 149(1), 24–39.
64.
SchweinbergerM.SnijdersT. A. B. (2007). Markov models for digraph panel data: Monte Carlo-based derivative estimation. Computational Statistics and Data Analysis, 51, 4465–4483.
65.
SinghJ. (2005). Collaborative networks as determinants of knowledge diffusion patterns. Management Science, 51(5), 756–770.
SnijdersT. A. B.LomiA.TorlòV. J. (2011). Multiplex dynamics of one-mode and two-mode networks, with an application to friendship and labor market preference(CORe Working Paper Series no.CORe-WP01-11). Lugano, Switzerland: University of Lugano.
68.
SnijdersT. A. B.KoskinenJ.SchweinbergerM. (2010). Maximum likelihood estimation for social network dynamics. The Annals of Applied Statistics, 4(2), 567–588.
69.
SnijdersT. A. B.SteglichC. E. G.SchweinbergerM. (2007). Modeling the co-evolution of networks and behavior. In Van MontfortK.OudJ.SatorraA. (Eds.), Longitudinal models in the behavioral and related sciences (pp. 41–72). Mahwah, NJ: Lawrence Erlbaum.
70.
SnijdersT. A. B.van de BuntG. G.SteglichC. E. G. (2010). Introduction to stochastic actor-based models for network dynamics. Social Networks, 32(1), 44–60.
71.
SorensonO.StuartT. E. (2008). Bringing the context back in: Settings and the search for syndicate partners in venture capital investment networks. Administrative Science Quarterly, 53(2), 266–294.
72.
van de BuntG. G.GroenewegenP. (2007). An actor-oriented dynamic network approach: The case of interorganizational network evolution. Organizational Research Methods, 10, 463–482.
73.
Von KroghG.SpaethS.LakhaniK. R. (2003). Community, joining, and specialization in open source software innovation: A case study. Research Policy, 32(7), 1217–1241.
74.
Von KroghG.Von HippelE. (2006). The promise of research on open source software. Management Science, 52(7), 975–983.
75.
WangP.SharpeK.RobinsG.PattisonP. (2009). Exponential random graph (p*) models for affiliation networks. Social Networks, 31(1), 12–25.
76.
WassermanS. (1980). Analyzing social networks as stochastic processes. Journal of the American Statistical Association, 75(370), 280–294.
77.
WassermanS.FaustK. (1994). Social network analysis: Methods and applications. Cambridge, MA: Cambridge University Press.
78.
ZhouT.RenJ.MedoM.ZhangY. C. (2007). Bipartite network projection and personal recommendation. Physical Review E, 76, 046115.
79.
Zwijze-KoningK. H.de JongM. D. T. (2005). Auditing information structures in organizations: A review of data collection techniques for network analysis. Organizational Research Methods, 8, 429–453.
