# IEEE Transactions on Network Science and Engineering

Includes the top 50 most frequently accessed documents for this publication according to the usage statistics for the month of

• ### Stability of Spreading Processes over Time-Varying Large-Scale Networks

Publication Year: 2016, Page(s):44 - 57
Cited by:  Papers (15)
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In this paper, we analyze the dynamics of spreading processes taking place over time-varying networks. A common approach to model time-varying networks is via Markovian random graph processes. This modeling approach presents the following limitation: Markovian random graphs can only replicate switching patterns with exponential inter-switching times, while in real applications these times are usua... View full abstract»

• ### Detecting Multiple Information Sources in Networks under the SIR Model

Publication Year: 2016, Page(s):17 - 31
Cited by:  Papers (2)
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In this paper, we study the problem of detecting multiple information sources in networks under the Susceptible-Infected-Recovered (SIR) model. First, assuming the number of information sources is known, we develop a sample-path-based algorithm, named clustering and localization, for trees. For $g$ View full abstract»

• ### On the Interplay Between Individuals’ Evolving Interaction Patterns and Traits in Dynamic Multiplex Social Networks

Publication Year: 2016, Page(s):32 - 43
Cited by:  Papers (4)
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The interplay between individuals' social interactions and traits has been studied extensively but traditionally from static or homogeneous social network perspectives. The recent availability of dynamic and heterogeneous (multiplex) network data has introduced a variety of new challenges. Critically, novel computational models are needed that can cope with data dynamics and heterogeneity. In this... View full abstract»

• ### Spreading Processes in Multilayer Networks

Publication Year: 2015, Page(s):65 - 83
Cited by:  Papers (29)
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Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while inf... View full abstract»

• ### A Mathematical Theory for Clustering in Metric Spaces

Publication Year: 2016, Page(s):2 - 16
Cited by:  Papers (3)
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Clustering is one of the most fundamental problems in data analysis and it has been studied extensively in the literature. Though many clustering algorithms have been proposed, clustering theories that justify the use of these clustering algorithms are still unsatisfactory. In particular, one of the fundamental challenges is to address the following question: What is a cluster in a set of data poi... View full abstract»

• ### A Brief Survey of PageRank Algorithms

Publication Year: 2014, Page(s):38 - 42
Cited by:  Papers (3)
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We examine several PageRank approximation algorithms. Quantitative analyses are provided to illustrate the extraordinary effectiveness of the PageRank computation. View full abstract»

• ### Random Walks, Markov Processes and the Multiscale Modular Organization of Complex Networks

Publication Year: 2014, Page(s):76 - 90
Cited by:  Papers (32)
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Most methods proposed to uncover communities in complex networks rely on combinatorial graph properties. Usually an edge-counting quality function, such as modularity, is optimized over all partitions of the graph compared against a null random graph model. Here we introduce a systematic dynamical framework to design and analyze a wide variety of quality functions for community detection. The qual... View full abstract»

• ### Data-Driven Network Resource Allocation for Controlling Spreading Processes

Publication Year: 2015, Page(s):127 - 138
Cited by:  Papers (2)
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We propose a mathematical framework, based on conic geometric programming, to control a susceptible-infected-susceptible viral spreading process taking place in a directed contact network with unknown contact rates. We assume that we have access to time series data describing the evolution of the spreading process observed by a collection of sensor nodes over a finite time interval. We propose a d... View full abstract»

• ### Sync-Rank: Robust Ranking, Constrained Ranking and Rank Aggregation via Eigenvector and SDP Synchronization

Publication Year: 2016, Page(s):58 - 79
Cited by:  Papers (4)
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We consider the classical problem of establishing a statistical ranking of a set of n items given a set of inconsistent and incomplete pairwise comparisons between such items. Instantiations of this problem occur in numerous applications in data analysis (e.g., ranking teams in sports data), computer vision, and machine learning. We formulate the above problem of ranking with incomplete noisy info... View full abstract»

• ### Robust Network Routing under Cascading Failures

Publication Year: 2014, Page(s):53 - 66
Cited by:  Papers (8)
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We propose a dynamical model for cascading failures in single-commodity network flows. In the proposed model, the network state consists of flows and activation status of the links. Network dynamics is determined by a, possibly state-dependent and adversarial, disturbance process that reduces flow capacity on the links, and routing policies at the nodes that have access to the network state, but a... View full abstract»

• ### The Strategic Formation of Multi-Layer Networks

Publication Year: 2015, Page(s):164 - 178
Cited by:  Papers (3)
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We study the strategic formation of multi-layer networks, where each layer represents a different type of relationship between the nodes in the network and is designed to maximize some utility that depends on the topology of that layer and those of the other layers. We start by generalizing distance-based network formation to the two-layer setting, where edges are constructed in one layer (with fi... View full abstract»

• ### Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

Publication Year: 2015, Page(s):139 - 151
Cited by:  Papers (2)
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Multi-agent networks are often modeled as interaction graphs, where the nodes represent the agents and the edges denote some direct interactions. The robustness of a multi-agent network to perturbations such as failures, noise, or malicious attacks largely depends on the corresponding graph. In many applications, networks are desired to have well-connected interaction graphs with relatively small ... View full abstract»

• ### Bi-Virus SIS Epidemics over Networks: Qualitative Analysis

Publication Year: 2015, Page(s):17 - 29
Cited by:  Papers (4)
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The paper studies the qualitative behavior of a set of ordinary differential equations (ODE) that models the dynamics of bi-virus epidemics over bilayer networks. Each layer is a weighted digraph associated with a strain of virus; the weights gzίrepresent the rates of infection from node i to node j of strain z. We establish a sufficient condition on the g's that guarantees survival... View full abstract»

• ### Distributed Online Convex Optimization Over Jointly Connected Digraphs

Publication Year: 2014, Page(s):23 - 37
Cited by:  Papers (7)
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This paper considers networked online convex optimization scenarios from a regret analysis perspective. At each round, each agent in the network commits to a decision and incurs in a local cost given by functions that are revealed over time and whose unknown evolution model might be adversarially adaptive to the agent's behavior. The goal of each agent is to incur a cumulative cost over time with ... View full abstract»

• ### Robustness of Large-Scale Stochastic Matrices to Localized Perturbations

Publication Year: 2015, Page(s):53 - 64
Cited by:  Papers (1)
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Many notions of network centrality can be formulated in terms of invariant probability vectors of suitably defined stochastic matrices encoding the network structure. Analogously, invariant probability vectors of stochastic matrices allow one to characterize the asymptotic behavior of many linear network dynamics, e.g., arising in opinion dynamics in social networks as well as in distributed avera... View full abstract»

• ### Clustering Network Layers with the Strata Multilayer Stochastic Block Model

Publication Year: 2016, Page(s):95 - 105
Cited by:  Papers (11)
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Multilayer networks are a useful data structure for simultaneously capturing multiple types of relationships between a set of nodes. In such networks, each relational definition gives rise to a layer. While each layer provides its own set of information, community structure across layers can be collectively utilized to discover and quantify underlying relational patterns between nodes. To concisel... View full abstract»

• ### Enhancement of Synchronizability in Networks with Community Structure through Adding Efficient Inter-Community Links

Publication Year: 2016, Page(s):106 - 116
Cited by:  Papers (5)
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In this paper we propose a framework for enhancing synchronizability of networks with community structure through adding efficient inter-community links. Adding new inter-community links to a network with community structure usually improves its synchronizability. However, this is achieved by increasing communication cost in the network. Thus, the links should be designed in a way that adding them... View full abstract»

• ### An Efficient Curing Policy for Epidemics on Graphs

Publication Year: 2014, Page(s):67 - 75
Cited by:  Papers (12)
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We provide a dynamic policy for the rapid containment of a contagion process modeled as an SIS epidemic on a bounded degree undirected graph with $n$ nodes. We show that if the budget $r$ View full abstract»

• ### Sparse Solutions to the Average Consensus Problem via Various Regularizations of the Fastest Mixing Markov-Chain Problem

Publication Year: 2015, Page(s):97 - 111
Cited by:  Papers (3)
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In the consensus problem on multi-agent systems, in which the states of the agents represent opinions, the agents aim at reaching a common opinion (or consensus state) through local exchange of information. An important design problem is to choose the degree of interconnection of the subsystems to achieve a good trade-off between a small number of interconnections and a fast convergence to the con... View full abstract»

• ### Understanding Sequential User Behavior in Social Computing: To Answer or to Vote?

Publication Year: 2015, Page(s):112 - 126
Cited by:  Papers (2)
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Understanding how users participate is of key importance to social computing systems since their value is created from user contributions. In many social computing systems, users decide sequentially whether to participate or not and, if participate, whether to create a piece of content directly, i.e., answering, or to rate existing content, i.e., voting. Moreover, there exists an answering-voting ... View full abstract»

• ### Rigid Network Design Via Submodular Set Function Optimization

Publication Year: 2015, Page(s):84 - 96
Cited by:  Papers (4)
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We consider the problem of constructing networks that exhibit desirable algebraic rigidity properties, which can provide significant performance improvements for associated formation shape control and localization tasks. We show that the network design problem can be formulated as a submodular set function optimization problem and propose greedy algorithms that achieve global optimality or an esta... View full abstract»

• ### The Attention Automaton: Sensing Collective User Interests in Social Network Communities

Publication Year: 2015, Page(s):40 - 52
Cited by:  Papers (1)
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The vast quantity of information shared in social networks has brought us to an age of attention scarcity, where getting users to be attentive to a message is not a given. In fact, it has become the limiting factor in the consumption of information by end users. Understanding what captures the collective attention within a community of users in a social network is invaluable to many applications, ... View full abstract»

• ### Algorithmic Renormalization for Network Dynamics

Publication Year: 2015, Page(s):1 - 16
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The aim of this work is to give a full, elementary exposition of a recently introduced algorithmic technique for renormalizing dynamic networks. The motivation is the analysis of time-varying graphs. We begin by showing how an arbitrary sequence of graphs over a fixed set of nodes can be parsed so as to capture hierarchically how information propagates across the nodes. Equipped with parse trees, ... View full abstract»

• ### A Network Model for Vehicular Ad Hoc Networks: An Introduction to Obligatory Attachment Rule

Publication Year: 2016, Page(s):82 - 94
Cited by:  Papers (2)
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In the past few years, the study of complex networks has attracted the attention of researchers. Many real networks, ranging from technological networks such as the Internet to biological networks, have been considered as special types of complex networks. Through application of the network science, important structural properties of such networks have been analyzed and the mechanisms that form su... View full abstract»

• ### On the Influence of the Seed Graph in the Preferential Attachment Model

Publication Year: 2015, Page(s):30 - 39
Cited by:  Papers (4)
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We study the influence of the seed graph in the preferential attachment model, focusing on the case of trees. We first show that the seed has no effect from a weak local limit point of view. On the other hand, we conjecture that different seeds lead to different distributions of limiting trees from a total variation point of view. We take a first step in proving this conjecture by showing that see... View full abstract»

• ### Decoding Binary Node Labels from Censored Edge Measurements: Phase Transition and Efficient Recovery

Publication Year: 2014, Page(s):10 - 22
Cited by:  Papers (24)
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We consider the problem of clustering a graph G into two communities by observing a subset of the vertex correlations. Specifically, we consider the inverse problem with observed variables Y = BGx ⊕ Z, where BG 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.... View full abstract»

• ### Synchronization of Diffusively-Connected Nonlinear Systems: Results Based on Contractions with Respect to General Norms

Publication Year: 2014, Page(s):91 - 106
Cited by:  Papers (6)
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Contraction theory provides an elegant way to analyze the behavior of certain nonlinear dynamical systems. In this paper, we discuss the application of contraction to synchronization of diffusively interconnected components described by nonlinear differential equations. We provide estimates of convergence of the difference in states between components, in the cases of line, complete, and star grap... View full abstract»

• ### Reconstruction in the Labelled Stochastic Block Model

Publication Year: 2015, Page(s):152 - 163
Cited by:  Papers (3)
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The labelled stochastic block model is a random graph model representing networks with community structure and interactions of multiple types. In its simplest form, it consists of two communities of approximately equal size, and the edges are drawn and labelledat random with probability depending on whether their two endpoints belong to the same community or not. It has been conjectured in [1] tha... View full abstract»

• ### All-to-All Communication in Random Regular Directed Graphs

Publication Year: 2014, Page(s):43 - 52
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We consider networks formed by the union of M random 1-regular directed graphs. These graphs are also called permutation models in the literature. We first present a proof showing that the expansion factor of such graphs is greater than or equal to log N a.a.s when M > 4 log N; where N is the number of nodes in the network. The reason for considering such random graph models is their applicabil... View full abstract»

## Aims & Scope

The IEEE Transactions on Network Science and Engineering is committed to timely publishing of peer-reviewed technical articles that deal with the theory and applications of network science and the interconnections among the elements in a system that form a network. In particular, the IEEE Transactions on Network Science and Engineering publishes articles on understanding, prediction, and control of structures and behaviors of networks at the fundamental level.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
Dapeng Oliver Wu

University of Florida

Dept. of Electrical  &  Computer Engineering

P. O. Box 116130

Gainesville, FL 32611

Email: dpwu@ufl.edu