Lev Muchnik

Lev Muchnik is a professor at the Hebrew University of Jerusalem, School of Business Administration, Internet Studies Department. Between 2008 and 2012, he was Senior research scientist at the Information, Operations Management and Statistics Department of the Leonard N. Stern School on Business at New York University. Lev earned his Ph.D. in Physics from Bar Ilan University. His expertise lies in the collection and analysis of massive data sets representing large-scale social systems, and their modeling using tools borrowed from social sciences and statistical physics.

His recent work has been focused on theoretical and empirical problems related to the structure and evolution of social networks, as well as peer effects, the spread of behavioral norms, information diffusion, and other processes specific to networked environments. Jointly with collaborators, Lev developed a seminal method for the identification of peer influence in networks, and conducted large-scale randomized controlled experiments in online communities. His expertise includes the design of scalable microscopic simulations of complex multi-agent systems and time-series analysis, in particular of long-term memory and scaling characteristics of financial data.

Publications

• Muchnik, L., Aral, S., & Taylor, S. J. (2013). Social Influence Bias: A Randomized Experiment. Science, 341(6146), 647–651.
• Aral, S., Muchnik, L., & Sundararajan, A. (2013). Engineering Social Contagions: Optimal Network Seeding and Incentive Strategies. Network Science, Forthcoming.
• Muchnik, Lev, Pei, S., Parra, L. C., Reis, S. D. S., Andrade, J. S., Havlin, S., Makse, H. A. H. A., et al. (2013). Origins of power-law degree distribution in the heterogeneity of human activity in social networks. Scientific Reports, 3, 23. Physics and Society; Statistical Mechanics.
• Zia, K., Farrahi, K., Sharpanskykh, A., Ferscha, A., & Muchnik, L. (2013). Parallel and Distributed Simulation of Large-Scale Cognitive Agents. In Y. Demazeau et al. (Ed.), Advances on Practical Applications of Agents and Multi-Agent Systems (pp. 324–328). Springer, Heidelberg.
• Farrahi, K., Zia, K., Sharpanskykh, A., Ferscha, A., & Muchnik, L. (2013). Agent Perception Modeling for Movement in Crowds. PAAMS 2013.
• Kämpf, M., Kantelhardt, J. W., & Muchnik, L. (2012). From Time Series to Co-Evolving Functional Networks: Dynamics of the Complex System "Wikipedia”. ECCS 2012.
• Kämpf, M., Tismer, S., Kantelhardt, J. W., & Muchnik, L. (2012). Fluctuations in Wikipedia access-rate and edit-event data. Physica A: Statistical Mechanics and its Applications, 391(23), 6101–6111.
• Brot, H., Muchnik, L., Goldenberg, J., & Louzoun, Y. (2012). Feedback between node and network dynamics can produce real world network properties. Physica A: Statistical Mechanics and its Applications, null(null).
• Krawczyk, M. J., Muchnik, L., Mańka-Krasoń, A., & Kułakowski, K. (2011). Line graphs as social networks. Physica A: Statistical Mechanics and its Applications, 390(13), 2611–2618.
• Gallos, L., Kitsak, M., Havlin, S., & Liljeros, F. (2011). Why hubs may not be the most efficient spreaders. Bulletin of the American Physical Society.
• Itzhack, R., Muchnik, L., Erez, T., Tsaban, L., Goldenberg, J., Solomon, S., & Louzoun, Y. (2010). Empirical extraction of mechanisms underlying real world network generation. Physica A: Statistical Mechanics and its Applications, 389(22), 5308–5318.
• Kitsak, M., Gallos, L. K., Havlin, S., Liljeros, F., Muchnik, L., Stanley, H. E., & Makse, H. A. (2010). Identification of influential spreaders in complex networks. Nature Physics, 6(11), 888–893.
• Aral, S., Muchnik, L., & Sundararajan, A. (2009). Distinguishing influence-based contagion from homophily-driven diffusion in dynamic networks. PNAS, 106(51), 21544–21549.
• Muchnik, L, Bunde, A., & Havlin, S. (2009). Long term memory in extreme returns of financial time series. Physica A: Statistical Mechanics and its Applications, 388(19), Long term memory in extreme returns of financial
• Muchnik, Lev, & Sorin Solomon. (2007). Markov Nets and the NetLab Platform: Application to Continuous Double Auction. Complexity Hints for Economic Policy (pp. 157–180). Milano: Springer Milan.
• Muchnik, L, Itzhack, R., Solomon, S., & Louzoun, Y. (2007). Self-emergence of knowledge trees: Extraction of the Wikipedia hierarchies. Physical Review E, 76(1), 016106. Retrieved from
• Muchnik, L, Louzoun, Y., & Solomon, S. (2006). Agent Based Simulation Design Principles—Applications to Stock Market. In H. Takayasu (Ed.), Practical Fruits of Econophysics (pp. 183–188). Tokyo: Springer Tokyo.
• Louzoun, Y., Muchnik, L., & Solomon, S. (2006). Copying nodes versus editing links: the source of the difference between genetic regulatory networks and the WWW. Bioinformatics, 22(5), 581–588.
• Yamasaki, K., Muchnik, L., Havlin, S., Bunde, A., & Stanley, H. E. (2006). Scaling and memory in return loss intervals: Application to risk estimation. In H. Takayasu (Ed.), Practical Fruits of Econophysics (pp. 43–51). Tokyo: Springer.
• Daniel, G., Muchnik, L., & Solomon, S. (2006). Traders Imprint Themselves by Adaptively Updating their Own Avatar. In M. Beckmann, H. P. Künzi, G. Fandel, W. Trockel, A. Basile, A. Drexl, H. Dawid, et al. (Eds.), Artificial Economics (Vol. 564, pp. 27–38). Berlin/Heidelberg: Springer-Verlag.
• Blank, A., Alexandrowicz, G., Muchnik, L., Tidhar, G., Schneiderman, J., Virmani, R., & Golan, E. (2005). Miniature self-contained intravascular magnetic resonance (IVMI) probe for clinical applications. Magn Reson Med, 54(1), 105–112.
• Yamasaki, K., Muchnik, L., Havlin, S., Bunde, A., & Stanley, H. E. (2005). Scaling and memory in volatility return intervals in financial markets. Proc Natl Acad Sci U S A, 102(26), 9424–9428.
• Schneiderman, J., Wilensky, R. L., Weiss, A., Samouha, E., Muchnik, L., Chen-Zion, M., Ilovitch, M., et al. (2005). Diagnosis of thin-cap fibroatheromas by a self-contained intravascular magnetic resonance imaging probe in ex vivo human aortas and in situ coronary arteries. J Am Coll Cardiol, 45(12), 1961–1969.
• Muchnik, L, Slanina, F., & Solomon, S. (2003). The interacting gaps model: reconciling theoretical and numerical approaches to limit-order models. Physica A: Statistical Mechanics and its …, 330, 232–239.
• Muchnik, L, & Solomon, S. (2003). Statistical mechanics of conventional traders may lead to non-conventional market behavior. Physica Scripta, 41(T106).
• Shatner, M., Muchnik, L., Leshno, M., & Solomon, S. (2000). A continuous time asynchronous model of the stock market; beyond the LLS. Economic Dynamics from the Physics Point of View. Bad Honnef, Germany: Physikzentrum.