<![CDATA[ IEEE Transactions on Network Science and Engineering - new TOC ]]>
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TOC Alert for Publication# 6488902 2018June 21<![CDATA[Algebraic Connectivity Under Site Percolation in Finite Weighted Graphs]]>algebraic connectivity in a weighted graph that is subject to site percolation, random deletion of the vertices. Using a refined concentration inequality for random matrices we show in our main theorem that the (augmented) Laplacian of the percolated graph concentrates around its expectation. This concentration bound then provides a lower bound on the algebraic connectivity of the percolated graph. As a special case for $(n,d,lambda)$-graphs (i.e., $d$-regular graphs on $n$ vertices with all non-trivial eigenvalues of the adjacency matrix less than $lambda$ in magnitude) our result shows that, with high probability, the graph remains connected under a homogeneous site percolation with survival probability $pgeq1-C_{1}n^{-C_{2}/d}$ with $C_{1}$ and $C_{2}$ depending only on $lambda /d$ .]]>528691237<![CDATA[A Multi-Objective Evolutionary Algorithm for Promoting the Emergence of Cooperation and Controllable Robustness on Directed Networks]]> ${mathrm{MOEA}-mathrm{Net}}_{{mathrm{cc}}}$, has been devised to solve this problem. In the experiments, the performance of ${mathrm{MOEA}-mathrm{Net}}_{{mathrm{cc}}}$ is validated on both synthetic and real networks, and the results show that ${mathrm{MOEA}-mathrm{Net}}_{{mathrm{cc}}}$ can not only achieve balanced optimal results without changing degree distribution of networks; but also create diverse Pareto fronts, which provide various potential candidates for decision makers to deal with social and economic dilemmas.]]>5292100866<![CDATA[Analysis of Partial Diffusion LMS for Adaptive Estimation Over Networks with Noisy Links]]>521011121041<![CDATA[A Robust Advantaged Node Placement Strategy for Sparse Network Graphs]]>521131261159<![CDATA[Cascading Failures in Interdependent Systems: Impact of Degree Variability and Dependence]]>each system through aforementioned dependency. In particular, we examine the impact of the variability and dependence properties of node degrees on the probability of cascading failures. We show that larger variability in node degrees hampers widespread failures in the system, starting with random failures. Similarly, positive correlations in node degrees make it harder to set off an epidemic of failures, thereby rendering the system more robust against random failures.]]>52127140372<![CDATA[Networking the Boids Is More Robust Against Adversarial Learning]]>521411554473<![CDATA[Preventive and Reactive Cyber Defense Dynamics Is Globally Stable]]>cybersecurity dynamics approach aims to understand cybersecurity from a holistic perspective by modeling the evolution of the global cybersecurity state. These models describe the interactions between the various kinds of cyber attacks and the various kinds of cyber defenses that take place in complex networks. In this paper, we study a particular kind of cybersecurity dynamics caused by the interactions between two classes of attacks (called push-based attacks and pull-based attacks) and two classes of defenses (called preventive and reactive defenses). The dynamics was previously shown to be globally stable in a special regime of the parameter universe of a model with node-independent and edge-independent parameters, but little is known beyond this regime. In this paper, we prove that the dynamics is globally stable in the entire parameter universe of a more general model with node-dependent and edge-dependent parameters. This means that the dynamics always converges to a unique equilibrium. We also prove that the dynamics converges exponentially to the equilibrium except for a particular parameter regime, in which the dynamics converges polynomially. Since it is often difficult to compute the equilibrium, we propose bounds of the equilibrium and numerically show that these bounds are tighter than those proposed in the literature.]]>521561701033