From social networking to neural pathways to the increasingly networked structure of the economy, complex networks are an integral part of our lives. NECSI research on the structure and dynamics of networks expands our understanding of how biological, social, and technological networks behave.

Network Modeling

NECSI researchers developed general models describing how networked systems respond to disturbances.

The Limits of Phenomenology. Y. Bar-Yam (2015).

Dynamical Response of Networks Under External Perturbations: Exact Results. D. Chinellato, M. de Aguiar, I. Epstein, D. Braha, Y. Bar-Yam (2015).

Analytically solvable model of probabilistic network dynamics, M. A. M. de Aguiar, I. R. Epstein, Y. Bar-Yam (2005).

Spectral analysis and the dynamic response of complex networks, M. A. M. de Aguiar, Y. Bar-Yam (2005).

Response of complex networks to stimuli, Y. Bar-Yam, I. R. Epstein (2004).

Optimization of robustness and connectivity in complex networks, B. Shargel, H. Sayama, I. R. Epstein, Y. Bar-Yam (2003).


By describing the way regulatory networks behave we can describe the functioning of cells.

Dynamics of cellular level function and regulation derived from murine expression array data, B. de Bivort, S. Huang, Y. Bar-Yam (2004).


Understanding neural networks enables us to understand how thoughts happen, and how and why people do many things including why they need to sleep.

Dynamics of Complex Systems, Chapter 2. Y. Bar-Yam (1997).

Substructure in Complex Systems and Partially Subdivided Neural Networks I: Stability of Composite Patterns R. Sadr-Lahijany, Y. Bar-Yam.

Sleep as Temporary Brain Dissociation, Y. Bar-Yam (1993).


The complex systems models developed by NECSI are also applied to corporate competition and economic markets.

Corporate competition: A self-organized network. D. Braha, B. Stacey, and Y. Bar-Yam (July 2011).

Networks of Economic Market Interdependence and Systemic Risk. D. Harmon, B. Stacey, Yavni Bar-Yam, and Yaneer Bar-Yam (November 16, 2010).

Corporate Competition: A Self-Organizing Network. D. Braha, B. Stacey, Y. Bar-Yam (October 3, 2001).


NECSI studies the dynamics of social networks and their roles in societal movements.

Global Patterns of Synchronization in Human Communications, A.J. Morales, V. Vavilala, R.M. Benito, Y. Bar-Yam (March 1, 2017).

Analysis of Infectious-Recovery Epidemic Models for Membership Dynamics of Online Social Networks, D. Cooney, F. Prieto-Castrillo, Y. Bar-Yam (2016).

Dynamic Model of Time-Dependent Complex Networks. S. A. Hill, D. Braha (2010).

From centrality to temporary fame: dynamic centrality in complex networks, D. Braha, Y. Bar-Yam (2006).

An Exploration of Social Identity: The Geography and Politics of News-Sharing Communities in Twitter, A. HerdaÄźdelen, W. Zuo, A.S. Gard-Murray, Y. Bar-Yam (2013).

The transition from Search to Social Media: The future of information networks. Yaneer Bar-Yam.


NECSI analysis describes how information flows in socio-technological networks and why they are organized the way they are.

Preferential detachment in broadcast signaling networks: connectivity and cost trade-off, M. Lim, D. Braha, S. Wijesinghe, S. Tucker, Y. Bar-Yam (2007).

The statistical mechanics of complex product development: empirical and analytical results, D. Braha, Y. Bar-Yam (July, 2007).

Information Flow Structure in Large-Scale Product Development Organizational Networks, D. Braha, Y. Bar-Yam (2004).

Topology of large-scale engineering problem-solving networks, D. Braha, Y. Bar-Yam (2004).

Unusual percolation in simple small-world networks, R. Cohen, D.J. Dawid, M. Kardar, Y. Bar-Yam (2009).



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