DYSONET: A Study of Networks

1. DYSONET: A Study of Networks Human behaviour through dynamics of complex social networks: An interdisciplinary approach Sixth (6th) Framework Program STREP Project (STREP=Specific Targeted REsearch Project) Panos Argyrakis University of Thessaloniki Thessaloniki, GREECE

3. 7 THEMATIC AREAS (e.g. Nano, IST, etc) CROSS-CUTTING ACTIVITIES …….. New and Emerging Science and Technology (NEST) Initiatives: INSIGHT ADVENTURE PATHFINDER Synthetic Biology Tackling Complexity in Science What it means to be human 2003 Call: DYSONET

4. This NEST: Tackling Complexity in Science(only 1 call, Ever !!) • Early stage funding of emerging research areas • Must be interdisciplinary • Must help identify and coordinate the community working on such problems by providing means of interaction • Fashionable areas: Biology, Social sciences, Environment • Originally planned for funding: 6-10 STREPS & 1 CA • Finally approved 11 out of 40 STREPS, 1 out of 3 CA • Criteria used for evaluation: Relevance, Excellence, Impact, Consortium, Financial aspects, Management

5. The consortium

6. Geographical distribution SU BU JLUG UA AUTH INFM BIU

7. Duration of the project : 36 months14.12.2004 – 13.12.2007 • “Coordination” means that EC wants to “see” and have only one (1) contact point, that of the Coordinator • Send any and all materials you have to the Coordination point only (Thessaloniki), NOT to Brussels directly • Project has a Project officer in Brussels: Dr. A. Martin-Hodbey • Consortium Agreement • e.g. IPR agreement (Intellectual Property Rights) • Ethical issues • NCP (National Contact Point) for every country

8. Budget RTD Activities: 1,488,798.00 Management activities: 90,000.00

9. Indicative budget breakdown

10. Workplan

11. Data collection Network model characterization Designs for network optimization Analysis of real-world networks Dissemination of results Management

12. Deliverables

13. DYSONET Summary • Develop the basic science of complex social networks in a quantitative way, • Apply our findings to the dynamics of human behavior, and generalize to a wider range of networks, including economic, traffic, and environmental networks. • Methods and techniques based on Statistical Physics concepts • Quantitative characterization of complex social networks by analyzing a number of real-world phenomena • Dynamic patterns in other disciplines, such as Economics and Finance, and Environmental networks. • Characterize, optimize and control the structure, dynamics and flow in complex social networks • Our studies will use extensive real-world data of social nature, which will be collected in the frame of the project.

14. Objectives • To focus and develop further the science and applications of the dynamics of complex social networks • To apply similar techniques and investigate 2 or 3 other key systems, such as networks in econophysics, traffic and environment, thus showing the generality of our methods • To provide our developed tools and methods to other fields, by making them widely available.

15. Methodology(1) Real-world data collection • Collect large scale real-world data for social networks in Sweden: • daily social contacts (target: 8 million individuals) • flow of patients between and within hospitals (target: 300000 people) • sexual relationships (target: 10000 people) • Email, collaboration and trust networks data • Environmental network • Finance data. Networks among firms or assets. Possibility to detect alternative ways to model factual connections among firms.

16. Methodology(2) Network model characterization • Develop network models and characterize critical properties of these networks: • Robustness against attacks and failures. Critical threshold. Percolation theory against different types of attacks. • Capability for network flow. “Optimal path” on the network: length, the time or energy or price needed to traverse it. • Statistical methods such as scaling theory, percolation theory, critical phenomena, and renormalization approaches to analyze the structure, stability and dynamics of different network types under conditions of random and intentional attack. • Applications to social networks, as well as to the disruption and protection of networks. • Extensive large-scale computer simulations to determine static and dynamic properties. Use of Grid resources! • Develop new analytical, numerical and simulation techniques which will allow us to effectively characterize the models.

17. Methodology(3) Designs for network optimization • Identify designs of network models which are optimized for robustness and flow. • Initially, identify designs which optimize these quantities independently. • For robustness, networks which are optimized against simultaneous attacks and random failures. • Define a metric which represents a combination of these quantities and then identify designs which optimize both of robustness and flow simultaneously. • We will also develop strategies to improve stability and transport in a given network.

18. Methodology(4) Analysis of real-world networks • Apply our finding of the phases 2 and 3 to the real world networks of phase 1, and generalize. • Identify and validate network model. Static properties of the network are consistent with the model. • Determinenetwork robustness and flow capability. Compare against optimal designs. Are real world networks close to optimal design? If not, what principles drive the network to its design? • Proposal of network formation mechanisms. Decisions made locally and determine the overall network structure. The key will be to identify information available locally to individuals and the decisions they make.

19. Methodology(4) Analysis of real-world networks • Determine additional underlying principles common to all systems. Focus on: • Origins of the collective human behavior. What characteristics cause collective behavior? Key parameters characterizing connections and interactions? For what values of these parameters does collective behavior emerge? • Patterns of collective human behavior. What types of patterns in space and time are formed by the collective behavior? • Optimization of emergent collective behavior. To what extent does the collective behavior optimize key variables? How can parameters of a system be changed to meet optimization goals?

20. Methodology(4) Analysis of real-world networks • Expand to other systems: • Similar questions for Econophysics: • Human behavior through stocks trading. • Correlation-based networks of the market portfolio. • Topology of efficient stock exchanges and emerging ones. • Same analysis will be attempted in some commodity market. • Compare social and economic networks. Common methods? Common features? If the results are encouraging, extend to other types of networks, as well.

21. Real-world phenomena to study • Crowd behavior: strategies to evacuate people and stop panic. • Search strategies: efficient networks for searching objects and people. • Traffic flow: optimization of collective flow. • Dynamics of collaboration: human relationship networks such as collaboration, opinion propagation and email networks. • Spread of epidemics: efficient immunization strategies. • Patterns in economics and finance: dynamic patterns in other disciplines, such as Economics and Finance, and Environmental networks.

22. Crowd behavior • Panic-induced crowd stampedes. • Architects, engineers, etc • Statistical Physics approach. Strategy of an individual. • Understanding crowd network behavior towards efficient approaches to evacuate crowds. Optimal design of large common areas. Mechanisms through which cooperation increases effectiveness. Important questions • “What effect does feedback from others have on the behavior of an individual?” • “Can we effectively control the spread of panic by introducing a system of geometrical constraints or sociological factors?”

23. Search strategies • Optimization of random searching. • Identifying strategies by which individuals can best discover sparsely and randomly located food, for example. • Optimization of foraging behavior of many competing or cooperating individuals. Help design effective collective search methods such as for searching for lost people. • Possibility of searching individuals in society based on their personal properties and tendencies, Search in the virtual space of possible personal properties. • Applications of such strategies may be the understanding of the formation of groups in society as well as understanding the process of searching for jobs, experts in a field, acquaintances and the like.

24. Traffic flow • Traffic flow is an example of collective behavior. Drivers listen the same radio traffic reports. Emergence of collective behavior, causing even stronger bottlenecks on a previously optimal route. • Traffic flow is but one example of flow as emergent collective behavior. Almost anything can “flow” in a complex system: Hinder some flows and optimize others. Studying the optimization of collective behavior in flow in complex systems has profound implications for a number of critical problems, such as the distribution of food and medical aid or the spread of epidemic disease.

25. Dynamics of collaboration • Collaboration takes place on an underlying network of relationships. • Indirect methods of identifying relationships • We will characterize topology, dynamics of the network and flow in the network and identify the organizing principles of the collective behavior. • Determine if the networks represent an optimization of the critical parameters. Study the dynamics of propagation of new successful scientific concepts and ideas by using keywords and analyze their evolution with time in the network collaboration. • Study collaboration patterns via indirect routes, e.g. Email network. Time evolution of the network in the aim to find patterns that will help modeling and better understanding of this network. How clustering is formed dynamically in the email network.

26. Spread of epidemics • Spreading of epidemics is an extremely important public health issue. Absence of critical threshold. Clearly a collective phenomenon, where the underlying structure determines the fate of the disease evolution. • Strategies of immunization. • Determine optimum immunization strategies, depending on the particular structure of a given population, and interdependence of network topology and epidemics, i.e. restructure a given network to protect against a disease spread. Similar: enhancing the spread on such a network. • It is similar to traffic flow, but e.g. the probability of spreading and the presence of immune nodes do not appear in the problem of traffic flow.

27. Patterns in economic and finance • Markets are a collective phenomenon characterized by cooperation and competition, specialization and globalization. • Emergent collective behavior in the economy is manifested in various ways, such as self-adaptation. • Feedback mechanisms. • Significant open questions include: • “How does one map out these structures from financial data?” • “What is the role of such structures in the feedback mechanisms causing major changes on the market and how can those changes be quantitatively characterized and controlled?” • We will investigate the statistical robustness of these networks by estimating quantitatively the effects of noise dressing on their stability. • Dynamics of the portfolio (and/or of the entire market) both on elements which are acting as “hubs” of the financial networks and elements which are acting as branches of it. Conclusion about the “stability” of the considered portfolio could therefore eventually be extrapolated.

28. Potential Impact of DYSONET • Applications are mainly related to the dynamics of human behavior • The basic science and the methods developed will be applicable to a broad range of phenomena (e.g. the robustness of power grids, the spreads of rumors and epidemics, efficient immunization, efficient searching, etc). • Important questions to be answered: (a) What are the fundamental properties that characterize the type of a network? (b) What mathematical models represent a given real world network? (c) How are stability, robustness, dynamics and transport properties of these models characterized? (d) What network design strategies can be developed to optimize the stability, robustness, and efficiency of transport on these networks? • Other direct useful benefits: (a) Efficient approaches for immunization and panic control. (b) Design of optimal networks, for tasks such as searching people and distributing goods. (c) Optimal approaches for crowd evacuation during panic situations, such as in cases of earthquakes, terrorist attacks, etc.