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Popular Articles Alert for this Publication# 6488902 2017February <![CDATA[Information Propagation in Clustered Multilayer Networks]]>34211224677<![CDATA[Suppressing Epidemics in Networks Using Priority Planning]]>34271285906<![CDATA[The Minimum Information Dominating Set for Opinion Sampling in Social Networks]]>34299311894<![CDATA[Design of Self-Organizing Networks: Creating Specified Degree Distributions]]>33147158483<![CDATA[Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations]]> per unit (p.u.), and (b) the minimal amount of load that must be shed in order to restore the grid to stable operation. We describe optimization formulations of the problem of finding the most disruptive attack, which are either nonlinear programing problems or nonlinear bilevel optimization problems, and describe customized algorithms for solving these problems. Experimental results on the IEEE 118-Bus system and a Polish 2383-Bus system are presented.]]>331321461088<![CDATA[Information Diffusion in Social Networks in Two Phases]]>34197210710<![CDATA[The Smallest Eigenvalue of the Generalized Laplacian Matrix, with Application to Network-Decentralized Estimation for Homogeneous Systems]]>343123243593<![CDATA[The Strategic Formation of Multi-Layer Networks]]>24164178425<![CDATA[Algorithmic Renormalization for Network Dynamics]]>21116461<![CDATA[Spreading Processes in Multilayer Networks]]>226583686<![CDATA[Clustering Network Layers with the Strata Multilayer Stochastic Block Model]]>32951051900<![CDATA[A Brief Survey of PageRank Algorithms]]>113842129<![CDATA[Cascading Failures in Load-Dependent Finite-Size Random Geometric Networks]]>341831961339<![CDATA[An Efficient Curing Policy for Epidemics on Graphs]]> nodes. We show that if the budget of curing resources available at each time is , where is the CutWidth of the graph, and also of order , then the expected time until the extinction of the epidemic is of order , which is within a constant factor from optimal, as well as sublinear in the number of nodes. Furthermore, if the CutWidth increases only sublinearly with , a sublinear expected time to extinction is possible with a sublinearly increasing budget .]]>126775218<![CDATA[Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs]]>24139151942<![CDATA[Phase-Coupled Oscillators with Plastic Coupling: Synchronization and Stability]]>34240256671<![CDATA[On Propagation of Phenomena in Interdependent Networks]]>342252391459<![CDATA[Analyses of the Clustering Coefficient and the Pearson Degree Correlation Coefficient of Chung's Duplication Model]]>33117131698<![CDATA[Competitive Propagation: Models, Asymptotic Behavior and Quality-Seeding Games]]>PP9911820<![CDATA[Reciprocity and Efficiency in Peer Exchange of Wireless Nodes Through Convex Optimization]]>34257270681<![CDATA[Stability of Spreading Processes over Time-Varying Large-Scale Networks]]>3144571906<![CDATA[Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks]]>1276901164<![CDATA[Community Detection and Classification in Hierarchical Stochastic Blockmodels]]>411326611<![CDATA[All-to-All Communication in Random Regular Directed Graphs]]>114352312<![CDATA[Understanding Sequential User Behavior in Social Computing: To Answer or to Vote?]]>23112126713<![CDATA[A Network Model for Vehicular Ad Hoc Networks: An Introduction to Obligatory Attachment Rule]]>328294683<![CDATA[On the Emergence of Shortest Paths by Reinforced Random Walks]]>415569897<![CDATA[On the Interplay Between Individuals’ Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks]]>313243953<![CDATA[Data-Driven Network Resource Allocation for Controlling Spreading Processes]]>24127138453<![CDATA[Rigid Network Design Via Submodular Set Function Optimization]]>238496655<![CDATA[Distributed and Robust Fair Optimization Applied to Virus Diffusion Control]]>4141542325<![CDATA[The Coevolution of Appraisal and Influence Networks Leads to Structural Balance]]>342862981449<![CDATA[A Mathematical Theory for Clustering in Metric Spaces]]>cohesion measure in terms of the distance measure. Using the cohesion measure, we define a cluster as a set of points that are cohesive to themselves. For such a definition, we show there are various equivalent statements that have intuitive explanations. We then consider the second question: How do we find clusters and good partitions of clusters under such a definition? For such a question, we propose a hierarchical agglomerative algorithm and a partitional algorithm. Unlike standard hierarchical agglomerative algorithms, our hierarchical agglomerative algorithm has a specific stopping criterion and it stops with a partition of clusters. Our partitional algorithm, called the $K$-sets algorithm in the paper, appears to be a new iterative algorithm. Unlike the Lloyd iteration that needs two-step minimization, our $K$-sets algorithm only takes one-step minimization. One of the most interesting findings of our paper is the duality result between a distance measure and a cohesion measure. Such a duality result leads to a dual $K$ -sets algorithm for clustering a set of data points with a cohesion measure. The dual $K$-sets algorithm converges in the same way as a sequential version of the classical kernel $K$-means algorithm. The key difference is that a cohesion measure does not need to be positive semi-definite.]]>31216593<![CDATA[Efficient Multistate Route Computation]]>33171182698<![CDATA[Synchronization of Diffusively-Connected Nonlinear Systems: Results Based on Contractions with Respect to General Norms]]>1 or L^{∞} norms, must be introduced.]]>1291106509<![CDATA[Confidence Sets for the Source of a Diffusion in Regular Trees]]>412740521<![CDATA[Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization]]>3158792155<![CDATA[A Test of Hypotheses for Random Graph Distributions Built From EEG Data]]>PP99111132<![CDATA[Information Cascades in Feed-based Networks of Users with Limited Attention]]>PP99111675<![CDATA[Enhancement of Synchronizability in Networks with Community Structure through Adding Efficient Inter-Community Links]]>32106116390<![CDATA[Distributed Online Convex Optimization Over Jointly Connected Digraphs]]>112337548<![CDATA[Robustness of Large-Scale Stochastic Matrices to Localized Perturbations]]>225364443<![CDATA[Analysis of Centrality in Sublinear Preferential Attachment Trees via the Crump-Mode-Jagers Branching Process]]>41112362<![CDATA[Effective Network Quarantine with Minimal Restrictions on Communication Activities]]>331591701145<![CDATA[Decoding Binary Node Labels from Censored Edge Measurements: Phase Transition and Efficient Recovery]]>Gx ⊕ Z, where B_{G} is the incidence matrix of a graph G, x is the vector of unknown vertex variables (with a uniform prior), and Z is a noise vector with Bernoulli (ε) i.i.d. entries. All variables and operations are Boolean. This model is motivated by coding, synchronization, and community detection problems. In particular, it corresponds to a stochastic block model or a correlation clustering problem with two communities and censored edges. Without noise, exact recovery (up to global flip) of x is possible if and only the graph G is connected, with a sharp threshold at the edge probability log (n)/n for Erdos-Renyi random graphs. The first goal of this paper is to determine how the edge probabilityp needs to scale to allow exact recovery in the presence of noise. Defining the degree rate of the graph by α = np/log(n), it is shown that exact recovery is possible if and only if α > 2/(1 - 2ε)^{2} + o(1/(1 - 2ε)^{2}). In other words, 2/(1 - 2ε)^{2} is the information theoretic threshold for exact recovery at low-SNR. In addition, an efficient recovery algorithm based on semidefinite programming is proposed and shown to succeed in the threshold regime up to twice the optimal rate. For a deterministic graph G, defining the degree rate as α = d/log(n), where d is the minimum degree of the graph, it is shown that the proposed method achieves the rate α > 4((1 + λ)/(1 - λ)^{2}/(1 - 2ε)^{2} + o(1/(1 - 2ε)^{2}), where 1-λ is the spectral gap of the graph G.]]>111022362<![CDATA[Bi-Virus SIS Epidemics over Networks: Qualitative Analysis]]>211729499<![CDATA[On the Influence of the Seed Graph in the Preferential Attachment Model]]>213039280<![CDATA[Robust Network Routing under Cascading Failures]]>115366510<![CDATA[Incompatibility boundaries for properties of community partitions]]>PP9911342<![CDATA[Detecting Multiple Information Sources in Networks under the SIR Model]]>-regular trees, the estimators produced by the proposed algorithm are within a constant distance from the real sources with a high probability. We further present a heuristic algorithm for general networks and an algorithm for estimating the number of sources when the number of real sources is unknown.]]>311731981