We develop the concept of "dragon-kings'' corresponding to meaningful outliers, which are found to coexist with power laws in the distributions of event sizes under a broad range of conditions in a large variety of systems. These dragon-kings reveal the existence of mechanisms of...

In order to understand the different characteristics observed in real-world networks, one needs to analyze how and why networks form, the impact of network structure on agents' outcomes, and the evolution of networks over time. For this purpose, we combine a network game introduced by Ballester...

We study the evolution of networks when the creation and decay of links are based on the position of nodes in the network measured by their centrality. We show that the same network dynamics arises under various centrality measures, and solve analytically the network evolution. During the...

We consider a dynamic model of network formation where agents form and sever links based on the centrality of their potential partners. We show that the existence of capacity constrains in the amount of links an agent can maintain introduces a transition from dissortative to assortative...

By combining (i) the economic theory of rational expectation bubbles, (ii) behavioral finance on imitation and herding of investors and traders and (iii)the mathematical and statistical physics of bifurcations and phase transitions, the log-periodic power law (LPPL) model has been developed as a...

We examine the emergent field of economic networks and explore its ability to shed light on the global and volatile economy where credit, ownership, innovation, investment, and virtually every other economic activity is carried at a scale and scope that respects no geographical, organizational,...

We investigate topologically biased failure in scale-free networks with degree distribution P(k) ∝ k^−γ . The probability p that an edge remains intact is assumed to depend on the degree k of adjacent nodes i and j through p_ij ∝ (k_i k_j)^−α . By varying the exponent α, we interpolate...