Exact calculation of network robustness

Finding the most critical nodes regarding network connectivity has attracted the attention of many researchers in infrastructure networks, power grids, transportation networks and physics in complex networks. Static robustness of networks under intentional attacks analyses the ability of a system to maintain its connectivity after the disconnection or deletion of a series of targeted nodes. In this context, connectivity is typically measured by the size of the remaining largest connected component. When targeting these nodes, previous literature has mostly used adaptive strategies that sequentially remove central nodes, or created heuristics in order to improve the results of the adaptive strategies. The proposed methodology based on mathematical programming allows to identify, for every fraction of disconnected or removed nodes, the set that minimizes the size of the largest connected component of a network, i.e. it allows to calculate the exact (most critical) robustness of a network.Peer ReviewedPostprint (author's final draft

Content placement in 5G‐enabled edge/core data center networks resilient to link cut attacks

High throughput, resilience, and low latency requirements drive the development of 5G-enabled content delivery networks (CDNs) which combine core data centers (cDCs) with edge data centers (eDCs) that cache the most popular content closer to the end users for traffic load and latency reduction. Deployed over the existing optical network infrastructure, CDNs are vulnerable to link cut attacks aimed at disrupting the overlay services. Planning a CDN to balance the stringent service requirements and increase resilience to attacks in a cost-efficient way entails solving the content placement problem (CPP) across the cDCs and eDCs. This article proposes a framework for finding Pareto-optimal solutions with minimal user-to-content distance and maximal robustness to targeted link cuts, under a defined budget. We formulate two optimization problems as integer linear programming (ILP) models. The first, denoted as K-best CPP with minimal distance (K-CPP-minD), identifies the eDC/cDC placement solutions with minimal user-to-content distance. The second performs critical link set detection to evaluate the resilience of the K-CPP-minD solutions to targeted fiber cuts. Extensive simulations verify that the eDC/cDC selection obtained by our models improves network resilience to link cut attacks without adversely affecting the user-to-content distances or the core network traffic mitigation benefits.publishe

Identification of critical paralog groups with indispensable roles in the regulation of signaling flow

Extensive cross-talk between signaling pathways is required to integrate the myriad of extracellular signal combinations at the cellular level. Gene duplication events may lead to the emergence of novel functions, leaving groups of similar genes - termed paralogs - in the genome. To distinguish critical paralog groups (CPGs) from other paralogs in human signaling networks, we developed a signaling network-based method using cross-talk annotation and tissue-specific signaling flow analysis. 75 CPGs were found with higher degree, betweenness centrality, closeness, and ‘bowtieness’ when compared to other paralogs or other proteins in the signaling network. CPGs had higher diversity in all these measures, with more varied biological functions and more specific post-transcriptional regulation than non-critical paralog groups (non-CPG). Using TGF-beta, Notch and MAPK pathways as examples, SMAD2/3, NOTCH1/2/3 and MEK3/6-p38 CPGs were found to regulate the signaling flow of their respective pathways. Additionally, CPGs showed a higher mutation rate in both inherited diseases and cancer, and were enriched in drug targets. In conclusion, the results revealed two distinct types of paralog groups in the signaling network: CPGs and non-CPGs. Thus highlighting the importance of CPGs as compared to non-CPGs in drug discovery and disease pathogenesis

The minimum cost network upgrade problem with maximum robustness to multiple node failures

The design of networks which are robust to multiple failures is gaining increasing attention in areas such as telecommunications. In this paper, we consider the problem of upgrading an existent network in order to enhance its robustness to events involving multiple node failures. This problem is modeled as a bi-objective mixed linear integer formulation considering both the minimization of the cost of the added edges and the maximization of the robustness of the resulting upgraded network. As the robustness metric of the network, we consider the value of the Critical Node Detection (CND) problem variant which provides the minimum pairwise connectivity between all node pairs when a set of c critical nodes are removed from the network. We present a general iterative framework to obtain the complete Pareto frontier that alternates between the minimum cost edge selection problem and the CND problem. Two different approaches based on a cover model are introduced for the edge selection problem. Computational results conducted on different network topologies show that the proposed methodology based on the cover model is effective in computing Pareto solutions for graphs with up to 100 nodes, which includes four commonly used telecommunication networks.publishe

Investigating Topologic and Geometric Properties of Synthetic and Natural River Networks under Changing Climate

River networks are important landscape features that collect and transport water, sediment and nutrients from regions of higher elevation to lower elevations. These networks have been studied for several decades from a range of geomorphological and hydrological perspectives. Investigating the geometric and topological properties of river networks is important for developing predictive models describing the network dynamics under changing climate as well as for quantifying the physical processes operating upon them. Although these networks have been characterized for a wide range of geomorphic properties, topological properties, and in particular, spectral properties of river networks received limited attention. In this dissertation, we propose a framework to identify critical nodes on river networks in the context of vulnerability under external disruptions. In addition, through a graph-theoretic formulation of river network topology, we investigate the observed range of zero eigenvalues on the spectra using the notion of multiplicity, that can be related to controllability of the river network for a comprehensive understanding of the dynamics of a system under external forcing. Furthermore, we use topological and geometrical signatures of the river networks and their organizational complexity to study advection and diffusion of fluxes on the network. The findings of this research reveal great potential to understand external forcing, e.g. climatic, control on river networks\u27 geometric and topological properties