Robust-yet-fragile nature of interdependent networks

Uncovering the network structure of non-centrally cleared derivative markets: evidence from large regulatory data

The network structure of non-centrally cleared derivative markets, uncovered via the European Market Infrastructure Regulation, is investigated by reconstructing initial and variation margin networks to analyze channels of potential losses and liquidity dynamics. Despite the absence of central clearing, the derivative network is found to be ultra-small and a maximization-based filtering tool is proposed to identify channels in the network characterized by the highest exposures. I find these exposures to be mainly toward institutions outside the euro area, emphasizing the need for cooperation across different jurisdictions. Anomalous behavior in terms of diverging first and second moments of the degree and strength distributions are detected, signaling the presence of large exposures generating extreme liquidity outflows. A reference table of parameters' estimates based on real data is provided for different network sizes, with no break of confidentiality, making it possible to simulate in a realistic way the liquidity dynamic in global derivative markets even when access to supervisory data is not granted.

Improving robustness in interdependent networks under intentional attacks by optimizing intra-link allocation

The interdependent network is particularly vulnerable to attacks on high degree nodes; therefore, the improvement of its robustness under intentional attacks has become an important topic. In this paper, we put forward a new metric to quantify the robustness of interdependent networks against intentional attacks and develop an improved simulated annealing algorithm (ISAA) to maximize this metric by optimizing the allocation of intra-links in subnetworks. Based on the comparison between the ISAA and existing algorithms, it is found that the algorithm presented in this paper is more effective to enhance the robustness of an interdependent scale-free network (ISFN). By applying the ISAA to ISFNs with different coupling preferences, there is a key finding that the robustness of the optimized ISFN is significantly stronger than that of the original ISFN. In particular, for cases of disassortative and random couplings, no sudden collapse occurs in optimized ISFNs. According to the analysis of the degree and the clustering coefficient, we find that the subnetwork of the optimized ISFN exhibits an onion-like structure. In addition, the ISFN whose robustness is enhanced to resist the attacks on high degree nodes is still robust to the intentional attacks concerning the betweenness and PageRank.

Modelling cascading failures in networks with the harmonic closeness

Many studies on cascading failures adopt the degree or the betweenness of a node to define its load. From a novel perspective, we propose an approach to obtain initial loads considering the harmonic closeness and the impact of neighboring nodes. Based on simulation results for different adjustable parameter θ, local parameter δ and proportion of attacked nodes f, it is found that in scale-free networks (SF networks), small-world networks (SW networks) and Erdos-Renyi networks (ER networks), there exists a negative correlation between optimal θ and δ. By the removal of the low load node, cascading failures are more likely to occur in some cases. In addition, we find a valuable result that our method yields better performance compared with other methods in SF networks with an arbitrary f, SW and ER networks with large f. Moreover, the method concerning the harmonic closeness makes these three model networks more robust for different average degrees. Finally, we perform the simulations on twenty real networks, whose results verify that our method is also effective to distribute the initial load in different real networks.

Resilience of Urban Transport Network-of-Networks under Intense Flood Hazards Exacerbated by Targeted Attacks

Natural hazards including floods can trigger catastrophic failures in interdependent urban transport network-of-networks (NoNs). Population growth has enhanced transportation demand while urbanization and climate change have intensified urban floods. However, despite the clear need to develop actionable insights for improving the resilience of critical urban lifelines, the theory and methods remain underdeveloped. Furthermore, as infrastructure systems become more intelligent, security experts point to the growing threat of targeted cyber-physical attacks during natural hazards. Here we develop a hypothesis-driven resilience framework for urban transport NoNs, which we demonstrate on the London Rail Network (LRN). We find that topological attributes designed for maximizing efficiency rather than robustness render the network more vulnerable to compound natural-targeted disruptions including cascading failures. Our results suggest that an organizing principle for post-disruption recovery may be developed with network science principles. Our findings and frameworks can generalize to urban lifelines and more generally to real-world spatial networks.

Dynamic behavior analysis of an internet flow interaction model under cascading failures

Cascading failures in the internet have attracted recent attention due to their unpredictability and destructive consequences. Exploring the failure behavior patterns is necessary because they can provide effective intervention approaches to prevent huge network disasters. To analyze internet flow behaviors during cascading failures (chain reactions in router and link failures), we characterize the internet as two coupled networks, the router network and the flow network. Here the flow network is an abstract representation of data correlations obtained from the router network. We use this coupled network to build a cascading failure model for studying flow transmission and competition, which is reflected in bandwidth competition given by limited link capacity. We first study the dependency between routers and flows to explore the flow transmission efficiency when a failure event occurs. Moreover, we find that rerouting enables flow competition area (the number of flows with which one flow has a competitive relationship) to initially remain stable during a failure episode, but that it then quickly drops due to poor physical network connectivity. Additionally, in the early stage after the failure event, the degree of flow competition sharply increases because of the growing number of the flows and congestion. Subsequently, the flow competition decreases due to the failure of flow transmission.

Impact of Degree Heterogeneity on Attack Vulnerability of Interdependent Networks

The study of interdependent networks has become a new research focus in recent years. We focus on one fundamental property of interdependent networks: vulnerability. Previous studies mainly focused on the impact of topological properties upon interdependent networks under random attacks, the effect of degree heterogeneity on structural vulnerability of interdependent networks under intentional attacks, however, is still unexplored. In order to deeply understand the role of degree distribution and in particular degree heterogeneity, we construct an interdependent system model which consists of two networks whose extent of degree heterogeneity can be controlled simultaneously by a tuning parameter. Meanwhile, a new quantity, which can better measure the performance of interdependent networks after attack, is proposed. Numerical simulation results demonstrate that degree heterogeneity can significantly increase the vulnerability of both single and interdependent networks. Moreover, it is found that interdependent links between two networks make the entire system much more fragile to attacks. Enhancing coupling strength between networks can greatly increase the fragility of both networks against targeted attacks, which is most evident under the case of max-max assortative coupling. Current results can help to deepen the understanding of structural complexity of complex real-world systems.

Study of Robustness in Functionally Identical Coupled Networks against Cascading Failures

Based on coupled networks, taking node load, capacity and load redistribution between two networks into consideration, we propose functionally identical coupled networks, which consist of two networks connected by interlinks. Functionally identical coupled networks are derived from the power grid of the United States, which consists of many independent grids. Many power transmission lines are planned to interconnect those grids and, therefore, the study of the robustness of functionally identical coupled networks becomes important. In this paper, we find that functionally identical coupled networks are more robust than single networks under random attack. By studying the effect of the broadness and average degree of the degree distribution on the robustness of the network, we find that a broader degree distribution and a higher average degree increase the robustness of functionally identical coupled networks under random failure. And SF-SF (two coupled scale-free networks) is more robust than ER-ER (two coupled random networks) or SF-ER (coupled random network and scale-free network). This research is useful to construct robust functionally identical coupled networks such as two cooperative power grids.