References
- Abelson, H., G. Sussman, J. Sussman, and A. Perlis (1985). Structure and interpretation of computer programs, Volume 2. MIT Press Cambridge, MA.
- Ahmed, A., V. Batagelj, X. Fu, S.-H. Hong, D. Merrick, and A. Mrvar (2007). Visualisation and analysis of the internet movie database. In Visualization, 2007. APVIS’07. 2007 6th International Asia-Pacific Symposium on, pp. 17–24. IEEE.
- Albert, R., H. Jeong, and A. Barabási (2000). Error and attack tolerance of complex networks. Nature 406(6794), 378–382.
- Batagelj, V. and M. Zaveršnik (2011). Fast algorithms for determining (generalized) core groups in social networks. Advances in Data Analysis and Classification 5(2), 129–145.
- Beineke, L., O. Oellermann, and R. Pippert (2002). The average connectivity of a graph. Discrete mathematics 252(1-3), 31–45.
- Bolz, C., A. Cuni, M. Fijalkowski, and A. Rigo (2009). Tracing the meta-level: Pypy’s tracing jit compiler. In Proceedings of the 4th workshop on the Implementation, Compilation, Optimization of Object-Oriented Languages and Programming Systems, pp. 18–25. ACM.
- Brandes, U. and T. Erlebach (2005). Network analysis: methodological foundations, Volume 3418. Springer Verlag.
- Csárdi, G. and T. Nepusz (2006). The igraph software package for complex network research.
- Dodds, P., D. Watts, and C. Sabel (2003). Information exchange and the robustness of organizational networks. Proceedings of the National Academy of Sciences 100(21), 12516.
- Ellson, J., E. Gansner, L. Koutsofios, S. North, and G. Woodhull (2002). Graphviz—open source graph drawing tools. In Graph Drawing, pp. 594–597. Springer.
- Fortunato, S. (2010). Community detection in graphs. Physics Reports 486(3-5), 75–174.
- Grannis, R. (2009). Paths and semipaths: reconceptualizing structural cohesion in terms of directed relations. Sociological Methodology 39(1), 117–150.
- Granovetter, M. (1985). Economic action and social structure: the problem of embeddedness. American Journal of Sociology 91(3), 481.
- Gutwenger, C. and P. Mutzel (2001). A linear time implementation of spqr-trees. In Graph Drawing, pp. 77–90. Springer.
- Hagberg, A., D. Schult, and P Swart (2008, August). Exploring network structure, dynamics, and function using NetworkX. In Proceedings of the 7th Python in Science Conference (SciPy2008), Pasadena, CA USA, pp. 11–15.
- Hopcroft, J. and R. Tarjan (1974). Dividing a graph into triconnected components.
- Hunter, J. D. (2007). Matplotlib: A 2d graphics environment. Computing In Science & Engineering 9(3), 90–95.
- Jones, E., T. Oliphant, P. Peterson, et al. (2001). SciPy: Open source scientific tools for Python.
- Kamada, T. and S. Kawai (1989). An algorithm for drawing general undirected graphs. Information processing letters 31(1), 7–15.
- Kanevsky, A. (1993). Finding all minimum-size separating vertex sets in a graph. Networks 23(6), 533-541.
- Latapy, M., C. Magnien, and N. Vecchio (2008). Basic notions for the analysis of large twomode networks. Social Networks 30(1), 31–48.
- Lind, P., M. Gonzalez, and H. Herrmann (2005). Cycles and clustering in bipartite networks. Physical Review E 72(5), 56127.
- Mani, D. and J. Moody (2014). Moving beyond stylized economic network models: The hybrid world of the indian firm ownership network. American Journal of Sociology 119(6), pp. 1629–1669.
- Moody, J. (2004). The structure of a social science collaboration network: Disciplinary cohesion from 1963 to 1999. American Sociological Review 69(2), 213–238.
- Moody, J., D. McFarland, and S. Bender-deMoll (2005). Dynamic network visualization. American Journal of Sociology 110(4), 1206–1241.
- Moody, J. and D. White (2003). Social cohesion and embeddedness: A hierarchical conception of social groups. American Sociological Review 6δ(1), 103–28.
- Newman, M. (2003). The structure and function of complex networks. SIAM Review 45, 167.
- O’Mahony, S. and F Ferraro (2007). The emergence of governance in an open source community. The Academy of Management Journal 50(5), 1079–1106.
- Opsahl, T. (2011). Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks 34.
- Pérez, F. and B. E. Granger (2007, May). IPython: a System for Interactive Scientific Computing. Comput. Sci. Eng. 9(3), 21–29.
- Powell, W., D. White, K. Koput, and J. Owen-Smith (2005). Network dynamics and field evolution: The growth of interorganizational collaboration in the life sciences. American journal of sociology 110(4), 1132–1205.
- Robins, G. and M. Alexander (2004). Small worlds among interlocking directors: Network structure and distance in bipartite graphs. Computational & Mathematical Organization Theory 10(1), 69–94.
- Seidman, S. (1983). Network structure and minimum degree. Social networks 5(3), 269–287.
- Shwed, U. and P. Bearman (2010). The temporal structure of scientific consensus formation. American sociological review 75(6), 817–840.
- Tarjan, R. (1972). Depth-first search and linear graph algorithms. In Switching and Automata Theory, 1971., 12th Annual Symposium on, pp. 114–121. IEEE.
- Uzzi, B., L. Amaral, and F. Reed-Tsochas (2007). Small-world networks and management science research: a review. European Management Review 4(2), 77–91.
- Van Rossum, G. (1995). Python reference manual. Centrum voor Wiskunde en Informatica.
- Wasserman, S. and K. Faust (1994). Social network analysis: Methods and applications. Cambridge University Press.
- White, D. and F. Harary (2001). The cohesiveness of blocks in social networks: Node connectivity and conditional density. Sociological Methodology, 305–359.
- White, D. and M. Newman (2001). Fast approximation algorithms for finding node-independent paths in networks. Santa Fe Institute Working Papers Series.
- White, D., J. Owen-Smith, J. Moody, and W. Powell (2004). Networks, fields and organizations: micro-dynamics, scale and cohesive embeddings. Computational & Mathematical Organization Theory 10(1), 95–117.