1. Zhenhua Wu Advisor: H. E. StanleyBoston University Co-advisor: Lidia A. BraunsteinUniversidad Nacional de Mar del Plata Collaborators: Shlomo HavlinBar-Ilan University Vittoria ColizzaTurin, Italy Reuven CohenBar-Ilan University Percolation analysis on scale-free networks with correlated link weights Z. Wu, L. A. Braunstein, V. Colizza, R. Cohen, S. Havlin, H. E. Stanley, Phys. Rev. E 74, 056104 (2006) 12/4/2007

2. General question Do link weights affect the network properties? Outline 1.Motivation 2.Modeling approach q c : definition and simulations in the model p c : percolation threshold (define c and T c ) 3.Numerical results 4.Summary

3. - =-2.01 Properties of real-world networks Part 1. Motivation: Example: world-wide airport network (WAN) Large cities (hubs) have many routs k (degree) Link weight T ij is “# of passengers” Link weight T ij depends on degree k i and k j of airports i and j 0.5 T HK-P T HK-C Real-world networks: 1.Heterogeneous connectivity 2.Heterogeneous weights 3.Correlation between connectivity and weights A. Barrat, M. Barthélemy, R. Pastor-Satorras, and A. Vespignani, PNAS, 101, 3747 (2004).

4. What part of network is more important for traffic? Part 1. Motivation: Choose the links with the highest traffic Thickness of links: Traffic, ex: number of passengers How to choose the most import links? Bad choice Good choice Introduce rank-ordered percolation q c : critical q to break network Infinite system Finite system q c  1-p c q: fraction of links removed with lowest weights S: number of nodes in the largest connected cluster N: number of nodes We remove links in ascending order of weight, T What is effect of weights & correlation on percolation?

5. Why is it important? World-wide airport network is closely related to epidemic spreading such as the case of SARS [1]. Help to develop more effective immunization strategies. Biological networks such as the E. coli metabolic networks also has the same correlation between weights and nodes degree. [2] [1] V. Colizza et al., BMC Medicine 5, 34 (2007) [2] P. J. Macdonald et al., Europhys. Lett. 72, 308 (2005) Part 1. Motivation:

6. 6 Scale-free (SF) Weighted scale-free networks “hub” Part 2. Model: Power-law distribution Define the weight on each link: Definitions: x ij : Uniform distributed random numbers 0 < x ij < 1. k i : Degree of node i. T ij : Weight, ex, traffic, number of passengers in WAN. For WAN,  = 0.5  : controls correlation : maximum degree k=1 k=10

7. Effect of  12 6 8 8 6 4 0.08 0.17 0.13 0.17 0.25 Part 2. Model: The sign of  determines the nature of the hubs Ex: For

8. Percolation properties only depend on the sign of  In studies of percolation properties, what matters is the rank of the links according their weight. 12 6 8 8 6 4 144 36 64 36 16 1 3 2 2 3 4 Ranking of the links remains unchanged for different  of the same sign. Part 2. Model: For

9. Specific questions 1.Will the  change the critical fraction q c of a network? 2.Will the  change the universality class of a network? Part 2.

10. Comparison of q c for different  Scale-free networks with  > 0 have larger q c than networks with   0. Part 2. q c : simulations on the model (number of nodes N = 8,192) ~0.45 ~0.7 ~1.0 N=8,192 0 N/2 S q c : critical q to break network q: fraction of links removed with lowest weights

11. Critical degree distribution exponent, c R. Cohen, K. Erez, D. ben-Avraham, S. Havlin, Phys. Rev. Lett. 85, 4626 (2000) c = 3 for scale-free networks with  = 0 23 1 0 Scale-free networks with  = 0 c is the below which p c is zero and above which p c is finite Part 2. c : previous result Perfectly connected p c  1-q c Fraction of links remained to connect the whole network

12. What is the c for  < 0? Theoretical results c = 3 for  = 0 Numerical results for  < 0 Numerical results for  > 0 Part 2. c : question about c for  > 0 If c (  > 0)  c (  = 0) different universality classes!

13. How to find out p c = 0 for  > 0? Difficulties: Limit of numerical precision  hard to determine p c = 0 numerically. Correlation  hard to find analytical solution for p c. Solution: Analytical approach with numerical solution Part 2. percolation threshold p c :

14. T c, the critical weight at p c : the critical weight at which the system is at p c diverge Kill all the links critical weight Smallest weight Largest weight pcpc Network of our model Maximum degree: k max kill Part 2. T c : The divergence of T c tells us whether p c = 0

15. Numerical results Result: indicates c = 3 for  > 0 Part 3. Numerical Results

16. successive slope  Strong finite size effect In our simulation, we can only reach 10 6 << 10 14 Part 3. Numerical Results

17. Part 4. Summary For the first time, we proposed and analyzed a model that takes into account the correlation between weights and node degrees. The correlation between weight T ij and nodes degree k i and k j, which is quantified by , changes the properties of networks. Scale-free networks with  > 0, such as the WAN, have larger q c than scale-free networks with   0. Scale-free networks with  > 0 and  = 0 belong to the same university class (have the same c = 3)

18. Acknowledgements Advisor: H. E. Stanley Co-advisor: Lidia A. Braunstein Professor Shlomo Havlin E. López, S. Sreenivasan, Y. Chen, G. Li, M. Kitsak Special thanks: E. López, P. Ivanov,