Stability of a Giant Connected Component in a Complex Network

We analyze the stability of the network's giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems

Finding shortest and nearly shortest path nodes in large substantially incomplete networks

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions (PPI) and the Internet at the autonomous system (AS) level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks. We demonstrate the utility of latent-geometric path-finding in problems of cellular pathway reconstruction and communication security

Fundamental Concepts of Cyber Resilience: Introduction and Overview

Given the rapid evolution of threats to cyber systems, new management approaches are needed that address risk across all interdependent domains (i.e., physical, information, cognitive, and social) of cyber systems. Further, the traditional approach of hardening of cyber systems against identified threats has proven to be impossible. Therefore, in the same way that biological systems develop immunity as a way to respond to infections and other attacks, so too must cyber systems adapt to ever-changing threats that continue to attack vital system functions, and to bounce back from the effects of the attacks. Here, we explain the basic concepts of resilience in the context of systems, discuss related properties, and make business case of cyber resilience. We also offer a brief summary of ways to assess cyber resilience of a system, and approaches to improving cyber resilience.Comment: This is a preprint version of a chapter that appears in the book "Cyber Resilience of Systems and Networks," Springer 201

Nitrides as ammonia synthesis catalysts and as potential nitrogen transfer reagents

In this article, an overview of the application of selected metal nitrides as ammonia synthesis catalysts is presented. The potential development of some systems into nitrogen transfer reagents is also described

Operational resilience: concepts, design and analysis

Building resilience into today’s complex infrastructures is critical to the daily functioning of society andits ability to withstand and recover from natural disasters, epidemics, and cyber-threats. This studyproposes quantitative measures that capture and implement the definition of engineering resilienceadvanced by the National Academy of Sciences. The approach is applicable across physical, information,and social domains. It evaluates the critical functionality, defined as a performance function of time setby the stakeholders. Critical functionality is a source of valuable information, such as the integratedsystem resilience over a time interval, and its robustness. The paper demonstrates the formulation ontwo classes of models: 1) multi-level directed acyclic graphs, and 2) interdependent coupled networks.For both models synthetic case studies are used to explore trends. For the first class, the approach isalso applied to the Linux operating system. Results indicate that desired resilience and robustness levelsare achievable by trading off different design parameters, such as redundancy, node recovery time, andbackup supply available. The nonlinear relationship between network parameters and resilience levelsconfirms the utility of the proposed approach, which is of benefit to analysts and designers of complexsystems and networks

Stability of a giant connected component in a complex network

The article of record as published may be found at https://doi.org/10.1103/PhysRevE.97.012309We analyze the stability of the network’s giant connected component under impact of adverse events, which we model through the link percolation. Specifically, we quantify the extent to which the largest connected component of a network consists of the same nodes, regardless of the specific set of deactivated links. Our results are intuitive in the case of single-layered systems: the presence of large degree nodes in a single-layered network ensures both its robustness and stability. In contrast, we find that interdependent networks that are robust to adverse events have unstable connected components. Our results bring novel insights to the design of resilient network topologies and the reinforcement of existing networked systems.U.S. Defense Threat Reduction AgencyNational Science Foundation (NSF)Army Research Office (ARO)Grant CCF-1212778 (NSF)Grant IIS-1741355 (NSF)Grant W911NF-16-1-0391 (ARO)Grant W911NF-17-1-0491 (ARO

Finding shortest and nearly shortest path nodes in large substantially incomplete networks by hyperbolic mapping

Dynamic processes on networks, be it information transfer in the Internet, contagious spreading in a social network, or neural signaling, take place along shortest or nearly shortest paths. Computing shortest paths is a straightforward task when the network of interest is fully known, and there are a plethora of computational algorithms for this purpose. Unfortunately, our maps of most large networks are substantially incomplete due to either the highly dynamic nature of networks, or high cost of network measurements, or both, rendering traditional path finding methods inefficient. We find that shortest paths in large real networks, such as the network of protein-protein interactions and the Internet at the autonomous system level, are not random but are organized according to latent-geometric rules. If nodes of these networks are mapped to points in latent hyperbolic spaces, shortest paths in them align along geodesic curves connecting endpoint nodes. We find that this alignment is sufficiently strong to allow for the identification of shortest path nodes even in the case of substantially incomplete networks, where numbers of missing links exceed those of observable links. We demonstrate the utility of latent-geometric path finding in problems of cellular pathway reconstruction and communication security.</p