Bulletin of the American Physical Society
2007 APS March Meeting
Volume 52, Number 1
Monday–Friday, March 5–9, 2007; Denver, Colorado
Session P22: Focus Session: Social Dynamics and Scaling |
Hide Abstracts |
Sponsoring Units: GSNP Chair: Sidney Redner, Boston University Room: Colorado Convention Center 108 |
Wednesday, March 7, 2007 11:15AM - 11:51AM |
P22.00001: The impact of social network complexity: from collaboration teams to epidemics Invited Speaker: Recent years have witnessed a tremendous progress in the gathering of large scale social networks thanks to the development of new informatics tools and the increase in computational power. Networks which trace the activities and interactions of individuals, social patterns, transportation fluxes and population movements on a local and global scale have been analyzed and found to exhibit complex features encoded in large scale heterogeneity, self-organization and other properties typical of complex systems. We will review the complex features characterizing many of these networks and their impact on dynamical processes ranging from the establishment of collaboration teams and the emergence of consensus to the geographical behavior of large scale epidemics. [Preview Abstract] |
Wednesday, March 7, 2007 11:51AM - 12:03PM |
P22.00002: Modeling self-organization of communication and topology in Social Networks Kim Sneppen We introduce a model of self-organization of communication and topology in social networks with a feedback between different communication habits and the topology. To study this feedback, we let agents communicate to build a perception of a network and use this information to create strategic links. We observe a narrow distribution of links when the communication is low and a system with a broad distribution of links when the communication is high. We also analyze the outcome of chatting, cheating, and lying, as strategies to get better access to information in the network. Chatting, although only adopted by a few agents, gives a global gain in the system. Contrary, in a system with too many liars a global loss is inevitable. \newline \newline References: M. Rosvall and K. Sneppen. ``Modeling self-organization of communication and topology in social networks.'' Phys. Rev. E 74:16108 (2006) [Preview Abstract] |
Wednesday, March 7, 2007 12:03PM - 12:15PM |
P22.00003: ABSTRACT WITHDRAWN |
Wednesday, March 7, 2007 12:15PM - 12:27PM |
P22.00004: Large-scale Individual-based Models of Pandemic Influenza Mitigation Strategies Kai Kadau, Timothy Germann, Ira Longini, Catherine Macken We have developed a large-scale stochastic simulation model to investigate the spread of a pandemic strain of influenza virus through the U.S. population of 281 million people, to assess the likely effectiveness of various potential intervention strategies including antiviral agents, vaccines, and modified social mobility (including school closure and travel restrictions) [1]. The heterogeneous population structure and mobility is based on available Census and Department of Transportation data where available. Our simulations demonstrate that, in a highly mobile population, restricting travel after an outbreak is detected is likely to delay slightly the time course of the outbreak without impacting the eventual number ill. For large basic reproductive numbers R$_{0}$, we predict that multiple strategies in combination (involving both social and medical interventions) will be required to achieve a substantial reduction in illness rates. [1] T. C. Germann, K. Kadau, I. M. Longini, and C. A. Macken, Proc. Natl. Acad. Sci. (USA) \textbf{103}, 5935-5940 (2006). [Preview Abstract] |
Wednesday, March 7, 2007 12:27PM - 12:39PM |
P22.00005: Dynamics of epidemics outbreaks in heterogeneous populations Dirk Brockmann, Alejandro Morales-Gallardo, Theo Geisel The dynamics of epidemic outbreaks have been investigated in recent years within two alternative theoretical paradigms. The key parameter of mean field type of models such as the SIR model is the basic reproduction number $R_0$, the average number of secondary infections caused by one infected individual. Recently, scale free network models have received much attention as they account for the high variability in the number of social contacts involved. These models predict an infinite basic reproduction number in some cases. We investigate the impact of heterogeneities of contact rates in a generic model for epidemic outbreaks. We present a system in which both the time periods of being infectious and the time periods between transmissions are Poissonian processes. The heterogeneities are introduced by means of strongly variable contact rates. In contrast to scale free network models we observe a finite basic reproduction number and, counterintuitively a smaller overall epidemic outbreak as compared to the homogeneous system. Our study thus reveals that heterogeneities in contact rates do not necessarily facilitate the spread to infectious disease but may well attenuate it. [Preview Abstract] |
Wednesday, March 7, 2007 12:39PM - 12:51PM |
P22.00006: The effect of heterogeneity in infectivity and susceptibility on epidemic spread Joel Miller We consider the spread of an epidemic on a network with few short cycles. We develop analytical tools to determine the probability and final size of an epidemic when the infectiousness and/or susceptibility of individuals is heterogeneous. Using these tools, we find the distributions of infectiousness or susceptibility which maximize or minimize the size or probability of an epidemic [Preview Abstract] |
Wednesday, March 7, 2007 12:51PM - 1:03PM |
P22.00007: Phase diagram of the diffusive epidemic process Ronald Dickman, Daniel Souza Maia We study the absorbing-state phase transition in the one- dimensional diffusive epidemic process via mean-field theory and Monte Carlo simulation. In this model, particles of two species (A and B) hop on a lattice and undergo reactions B $\to$ A and A + B $\to$ 2B; the total particle number is conserved. A phase transition between the (absorbing) B-free state and an active state is observed as the parameters (reaction and diffusion rates, and total particle density) are varied. Mean-field theory reveals a surprising, nonmonotonic dependence of the critical recovery rate on the diffusion rate of B particles. A computational realization of the process faithful to the master equation the model is devised. Using the quasi-stationary simulation method we determine the order parameter and the survival time in systems of up to 4000 sites. Due to strong finite-size effects, the results converge only for large system sizes. We find no evidence for a discontinuous transition. Our results are consistent with the existence of three distinct universality classes, depending on whether A particles diffusive more rapidly, less rapidly, or at the same rate as B particles. We also perform quasi-stationary simulations of the triplet creation model, which yield results consistent with a discontinuous transition at high diffusion rates. [Preview Abstract] |
Wednesday, March 7, 2007 1:03PM - 1:15PM |
P22.00008: Epidemics on dynamic networks with spatial structure Leah Shaw, Ira Schwartz When a population is faced with an epidemic outbreak, individuals are likely to modify their social behavior to avoid exposure to the disease. Epidemic models that assume a fixed network of contacts do not address this phenomenon. We consider an extension of the model of Gross et al (PRL 96: 208701, 2006), in which the contact network is rewired dynamically so that susceptibles avoid contact with infectives. We add a spatial structure to the network and explore both the network geometry and the dynamics of the infection. [Preview Abstract] |
Wednesday, March 7, 2007 1:15PM - 1:27PM |
P22.00009: Proximity Networks and Epidemics Hasan Guclu, Zolt\'an Toroczkai We presented the basis of a framework to account for the dynamics of contacts in epidemic processes, through the notion of dynamic proximity graphs. By varying the integration time-parameter $T$, which is the period of infectivity one can give a simple account for some of the differences in the observed contact networks for different diseases, such as smallpox, or AIDS. Our simplistic model also seems to shed some light on the shape of the degree distribution of the measured people-people contact network from the EPISIM data. We certainly do not claim that the simplistic graph integration model above is a good model for dynamic contact graphs. It only contains the essential ingredients for such processes to produce a qualitative agreement with some observations. We expect that further refinements and extensions to this picture, in particular deriving the link-probabilities in the dynamic proximity graph from more realistic contact dynamics should improve the agreement between models and data. [Preview Abstract] |
Wednesday, March 7, 2007 1:27PM - 1:39PM |
P22.00010: Adaptive networks: the example of consensus formation. Balazs Kozma, Alain Barrat It is well known that the structure of a network can significantly influence the properties of the dynamical processes on them. Though, the interplay between a process and the network topology on adaptive networks is still an open question. Adaptive rewiring of links can happen in real life systems such as acquaintance networks where two people are more likely to maintain a social connection if their views and values are similar. Similar adaptation should also be observed in biological and ecological networks. In our study, we consider various systems modeling the consensus formation of people and try to identify the quantities that are relevant in determining the behavior of adaptive networks. [Preview Abstract] |
Wednesday, March 7, 2007 1:39PM - 1:51PM |
P22.00011: Social network analysis based on WWW search engine Sang Hoon Lee, Pan-Jun Kim, Yong-Yeol Ahn, Hawoong Jeong Recently, massive digital records have made it possible to analyze a huge amount of data in social sciences, one of which is social network theory. We investigate social networks between people by extracting information on the World Wide Web. Using famous search engines such as Google, we construct weighted social networks where the nodes are the names of people and the weight of each link is assigned as the number of web pages including both of the names attached to the link. The weight distribution is found to be quite broad with the heavy-tail. The strength of a node, defined as the sum of weights over the node, is strongly correlated with the number of web pages including the single node. We compare networks constructed by this method with real networks to test the reliability of the method. Furthermore, we suggest the quantity, called the effective degree, characterizing the homogeneity (or heterogeneity) of weight distribution for each node in the weighted network. Another way to quantify the importance of each node, based on the effective degree, is also introduced. [Preview Abstract] |
Wednesday, March 7, 2007 1:51PM - 2:03PM |
P22.00012: The voter model on an adaptive network. Beate Schmittmann, Izabella Benczik, Royce K.P. Zia, Sandor Benczik In social networks, friendships emerge and fade, as individuals change their opinions. We discuss a simple model of such a network, in which the individuals are modeled by Ising spins (taking just two values: up or down) on the nodes of the network, while their connections are modeled by the presence or absence of edges. Nodes and edges evolve simultaneously. The spins are updated according to a simple majority rule (the voter model). Then, any pair of spins is then connected by an edge with probability p (q) if they are in the same (different) state. Thus, the edges also become dynamic variables, correlated with the state of the nodes, and the network is termed ``adaptive.'' Using simulations and exact solutions, we find four phases in the thermodynamic limit. There are two absorbing states in which all nodes are in the same state (all up or down). Then, there is a disordered phase in which the nodes take random values, and a phase in which the system remembers its initial magnetization. For finite systems, only the two absorbing states survive in the long-time limit. Consequences for social networks will be discussed. [Preview Abstract] |
Wednesday, March 7, 2007 2:03PM - 2:15PM |
P22.00013: Resolution limit in community detection Marc Barthelemy, Santo Fortunato Understanding the relation between structure and function in a complex network is a fundamental issue for practical applications in many disciplines such as biology or sociology. An important step in this direction has been made with the identification of communities through a now widely used method relying on the optimization of a quantity called modularity. However, we will show here that modularity optimization fail to identify modules smaller than a scale which depends on the total size of the network and on the degree of interconnectedness of the modules, even in cases where modules are unambiguously defined. We will illustrate this with simple examples taken both in artificial and in real social, biological and technological networks for which we show that modularity optimization indeed does not resolve a large number of modules. Reference: S. Fortunato and M. Barthelemy, PNAS, in press. [Preview Abstract] |
Follow Us |
Engage
Become an APS Member |
My APS
Renew Membership |
Information for |
About APSThe American Physical Society (APS) is a non-profit membership organization working to advance the knowledge of physics. |
© 2024 American Physical Society
| All rights reserved | Terms of Use
| Contact Us
Headquarters
1 Physics Ellipse, College Park, MD 20740-3844
(301) 209-3200
Editorial Office
100 Motor Pkwy, Suite 110, Hauppauge, NY 11788
(631) 591-4000
Office of Public Affairs
529 14th St NW, Suite 1050, Washington, D.C. 20045-2001
(202) 662-8700