Bulletin of the American Physical Society
2005 APS March Meeting
Monday–Friday, March 21–25, 2005; Los Angeles, CA
Session H5: From Biological to Infrastructure Networks: Structure and Dynamics |
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Sponsoring Units: GSNP Chair: Laszlo Barabasi, Notre Dame University Room: LACC 502B |
Tuesday, March 22, 2005 8:00AM - 8:36AM |
H5.00001: Evolution and structure of the Internet: A statistical physics approach Invited Speaker: The talk will present an overview of the large-scale topological and dynamical properties of real Internet maps. First I will review the methodology used to obtain large scale maps of the Internet at the router and autonomous system level. Then I will discuss the statistical features and regularities observed in the large scale structure of the Internet and the importance of the dynamics in the formulation of adequate models. Finally the various results and models will be scrutinized in the light of a statistical theory that considers the map's incompleteness due to measurement biases. [Preview Abstract] |
Tuesday, March 22, 2005 8:36AM - 9:12AM |
H5.00002: Defense in Complex Networks Invited Speaker: Complex networks with a heterogeneous distribution of loads may undergo a global cascade of overload failures when highly loaded nodes or edges are removed due to attacks or failures. Since a small attack or failure has the potential to trigger a global cascade, a fundamental question regards the possible strategies of defense to prevent the cascade from propagating through the entire network. Here I introduce and investigate a costless strategy of defense based on a selective further removal of nodes and edges, right after the initial attack or failure. This intentional removal of network elements is shown to drastically reduce the size of the cascade. [Preview Abstract] |
Tuesday, March 22, 2005 9:12AM - 9:48AM |
H5.00003: What can we learn from the network approach in finance? Invited Speaker: Correlations between variations of stock prices reveal information about relationships between companies. Different methods of analysis have been applied to such data in order to uncover the taxonomy of the market. We use Mantegna's miminum spanning tree (MST) method for daily data in a dynamic way: By introducing a moving window we study the temporal changes in the structure of the network defined by this ``asset tree.'' The MST is scale free with a significantly changing exponent of the degree distribution for crash periods, which demonstrates the restructuring of the network due to the enhancement of correlations. This approach is compared to that based on what we call ``asset graphs:'' We start from an empty graph with no edges where the vertices correspond to stocks and then, one by one, we insert edges between the vertices according to the rank of their correlation strength. We study the properties of the creatred (weighted) networks, such as topologically different growth types, number and size of clusters and clustering coefficient. Furthermore, we define new tools like subgraph intensity and coherence to describe the role of the weights. We also investigate the time shifted cross correlation functions for high frequency data and find a characteristic time delay in many cases representing that some stocks lead the price changes while others follow them. These data can be used to construct a directed network of influence. [Preview Abstract] |
Tuesday, March 22, 2005 9:48AM - 10:24AM |
H5.00004: Robustness of metabolic networks: Flux balance analysis Invited Speaker: Biological systems are unimaginably complex, yet also highly robust to genetic perturbations on all levels of organization. For example, the cellular metabolism of the bacterium E. coli maintains its homeostasis, often with little or no effect on the biomass yield under a considerable portion of single gene knockouts. To address the interplay between the robustness of the final biomass yield and the underlying mechanisms in the intracellular metabolism, we identify the set of intracellular metabolites of which presences are essential for the cellular- level viability via flux balance analysis. These essential metabolites exhibit the quite different characteristics both in the topological and physiological aspects of the participating reactions, compared with the case for the non-essential ones. Most importantly, it is revealed that in viable case, production and consumption rates of each essential metabolite acquire their robustness responding to the genetic perturbations, by actively reorganizing the reaction fluxes for the ultimate robustness of the biomass yield. We also find that there is strong correlation between essentiality and flux fluctuation of metabolite under the gene deletion purturbations. [Preview Abstract] |
Tuesday, March 22, 2005 10:24AM - 11:00AM |
H5.00005: Information Horizons in Complex Networks Invited Speaker: We investigate how the structure constrain specific communication in social-, man-made and biological networks. We find that human networks of governance and collaboration are predictable on teat-a-teat level, reflecting well defined pathways, but globally inefficient (1). In contrast, the Internet tends to have better overall communication abilities, more alternative pathways, and is therefore more robust. Between these extremes are the molecular network of living organisms. Further, for most real world networks we find that communication ability is favored by topology on small distances, but disfavored at larger distances (2,3,4). We discuss the topological implications in terms of modularity and the positioning of hubs in the networks (5,6). Finally we introduce some simple models which demonstarte how communication may shape the structure of in particular man made networks (7,8). \newline \newline 1) K. Sneppen, A. Trusina, M. Rosvall (2004). Hide and seek on complex networks [cond-mat/0407055] \newline 2) M. Rosvall, A. Trusina, P. Minnhagen and K. Sneppen (2004). Networks and Cities: An Information Perspective [cond-mat/0407054]. In PRL. \newline 3) A. Trusina, M. Rosvall, K. Sneppen (2004). Information Horizons in Networks. [cond-mat/0412064] \newline 4) M. Rosvall, P. Minnhagen, K. Sneppen (2004). Navigating Networks with Limited Information. [cond-mat/0412051] \newline 5) S. Maslov and K. Sneppen (2002). Specificity and stability in topology of protein networks Science 296, 910-913 [cond-mat/0205380]. \newline 6) A. Trusina, S. Maslov, P. Minnhagen, K. Sneppen Hierarchy Measures in Complex Networks. Phys. Rev. Lett. 92, 178702 [cond-mat/0308339]. \newline 7) M. Rosvall and K. Sneppen (2003). Modeling Dynamics of Information Networks. Phys. Rev. Lett. 91, 178701 [cond-mat/0308399]. \newline 8) B-J. Kim, A. Trusina, P. Minnhagen, K. Sneppen (2003). Self Organized Scale-Free Networks from Merging and Regeneration. nlin.AO/0403006. In European Journal of Physics. [Preview Abstract] |
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