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# IEEE Transactions on Network Science and Engineering

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• ### Fast Generation of Spatially Embedded Random Networks

Publication Year: 2017, Page(s):112 - 119
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Spatially Embedded Random Networks such as the Waxman random graph have been used in many settings for synthesizing networks. Prior to our work, there existed no software for generating these efficiently. Existing techniques are $O(n^2)$ where ... View full abstract»

• ### Information Propagation in Clustered Multilayer Networks

Publication Year: 2016, Page(s):211 - 224
Cited by:  Papers (3)
| | PDF (677 KB) | HTML

In today's world, individuals interact with each other in more complicated patterns than ever. Some individuals engage through online social networks (e.g., Facebook, Twitter), while some communicate only through conventional ways (e.g., face-to-face). Therefore, understanding the dynamics of information propagation among humans calls for a multi-layer network model where an online social network ... View full abstract»

• ### Community Detection and Classification in Hierarchical Stochastic Blockmodels

Publication Year: 2017, Page(s):13 - 26
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In disciplines as diverse as social network analysis and neuroscience, many large graphs are believed to be composed of loosely connected smaller graph primitives, whose structure is more amenable to analysis We propose a robust, scalable, integrated methodology for community detection and community comparison in graphs. In our procedure, we first embed a graph into an appropriate Euclidean space ... View full abstract»

• ### Diffusion of Innovation in Large Scale Graphs

Publication Year: 2017, Page(s):100 - 111
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Will a new smartphone application diffuse deeply in the population or will it sink into oblivion soon? To predict this, we argue that common models of spread of innovations based on cascade dynamics or epidemics may not be fully adequate. In this paper, we model the spread of a new technological item in a population through a novel network dynamics where diffusion is based on the word-of-mouth and... View full abstract»

Publication Year: 2017, Page(s):129 - 139
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Large-scale cascading failures can be triggered by very few initial failures, leading to severe damages in complex networks. This paper studies load-dependent cascading failures in random networks consisting of a large but finite number of components. Under a random single-node attack, a framework is developed to quantify the damage at each stage of a cascade. Estimations and analyses for the frac... View full abstract»

• ### Competitive Propagation: Models, Asymptotic Behavior and Quality-Seeding Games

Publication Year: 2017, Page(s):83 - 99
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In this paper we propose a class of propagation models for multiple competing products over a social network. We consider two propagation mechanisms: social conversion and self conversion, corresponding, respectively, to endogenous and exogenous factors. A novel concept, the product-conversion graph, is proposed to characterize the interplay among competing products. According to the chronological... 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 (3)
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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»

• ### Stochastic Subgradient Algorithms for Strongly Convex Optimization over Distributed Networks

Publication Year: 2017, Page(s): 1
| | PDF (868 KB)

We study diffusion and consensus based optimization of a sum of unknown convex objective functions over distributed networks. The only access to these functions is through stochastic gradient oracles, each of which is only available at a different node; and a limited number of gradient oracle calls is allowed at each node. In this framework, we introduce a convex optimization algorithm based on st... View full abstract»

• ### How Complex Contagions in Preferential Attachment Models and Other Time-Evolving Networks

Publication Year: 2017, Page(s): 1
| | PDF (668 KB)

The $k$-complex contagion model is a social contagion model which describes the diffusion of behaviors in networks where the successful adoption of a behavior requires influence from multiple contacts. It has been argued that $k$-complex contagions better model behavioral changes such as adoption of new beliefs, fashion trends or expensiv... View full abstract»

• ### Distributed and Robust Fair Optimization Applied to Virus Diffusion Control

Publication Year: 2017, Page(s):41 - 54
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This paper proposes three novel nonlinear, continuous-time, distributed algorithms to solve a class of fair resource allocation problems, which allow an interconnected group of operators to collectively minimize a global cost function subject to equality and inequality constraints. The proposed algorithms are designed to be robust so that temporary errors in communication or computation do not cha... View full abstract»

• ### Design of Self-Organizing Networks: Creating Specified Degree Distributions

Publication Year: 2016, Page(s):147 - 158
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A key problem in the study and design of complex systems is the apparent disconnection between the microscopic and the macroscopic. It is not straightforward to identify the local interactions that give rise to an observed global phenomenon, nor is it simple to design a system that will exhibit some desired global property using only local knowledge. Here we propose a methodology that allows for t... View full abstract»

• ### On the Emergence of Shortest Paths by Reinforced Random Walks

Publication Year: 2017, Page(s):55 - 69
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The co-evolution between network structure and functional performance is a fundamental and challenging problem whose complexity emerges from the intrinsic interdependent nature of structure and function. Within this context, we investigate the interplay between the efficiency of network navigation (i.e., path lengths) and network structure (i.e., edge weights). We propose a simple and tractable mo... View full abstract»

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

Publication Year: 2015, Page(s):84 - 96
Cited by:  Papers (3)
| | PDF (655 KB) | HTML

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»

• ### Network Maximal Correlation

Publication Year: 2017, Page(s): 1
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We introduce Network Maximal Correlation (NMC) as a multivariate measure of nonlinear association among random variables. NMC is defined via an optimization that infers transformations of variables by maximizing aggregate inner products between transformed variables. For finite discrete and jointly Gaussian random variables, we characterize a solution of the NMC optimization using basis expansion ... View full abstract»

• ### A Test of Hypotheses for Random Graph Distributions Built from EEG Data

Publication Year: 2017, Page(s):75 - 82
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The theory of random graphs has been applied in recent years to model neural interactions in the brain. While the probabilistic properties of random graphs has been extensively studied, the development of statistical inference methods for this class of objects has received less attention. In this work we propose a non-parametric test of hypotheses to test if a sample of random graphs was generated... View full abstract»

• ### Information Cascades in Feed-Based Networks of Users with Limited Attention

Publication Year: 2017, Page(s):120 - 128
| | PDF (1253 KB) | HTML

We build a model of information cascades on feed-based networks, taking into account the finite attention span of users, message generation rates and message forwarding rates. Through simulation of this model, we study the effect of the extent of user attention on the probability that the cascade becomes viral. In analogy with a branching process, we estimate the branching factor associated with t... View full abstract»

• ### The Smallest Eigenvalue of the Generalized Laplacian Matrix, with Application to Network-Decentralized Estimation for Homogeneous Systems

Publication Year: 2016, Page(s):312 - 324
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The problem of synthesizing network-decentralized observers arises when several agents, corresponding to the nodes of a network, exchange information about local measurements to asymptotically estimate their own state. The network topology is unknown to the nodes, which can rely on information about their neighboring nodes only. For homogeneous systems, composed of identical agents, we show that a... View full abstract»

• ### Information Diffusion in Social Networks in Two Phases

Publication Year: 2016, Page(s):197 - 210
| | PDF (710 KB)

The problem of maximizing information diffusion, given a certain budget expressed in terms of the number of seed nodes, is an important topic in social networks research. Existing literature focuses on single phase diffusion where all seed nodes are selected at the beginning of diffusion and all the selected nodes are activated simultaneously. This paper undertakes a detailed investigation of the ... View full abstract»

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

Publication Year: 2016, Page(s):95 - 105
Cited by:  Papers (1)
| | PDF (1900 KB) | HTML

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»

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

Publication Year: 2015, Page(s):17 - 29
Cited by:  Papers (3)
| | PDF (499 KB) | HTML

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»

• ### Hyperbolic Embedding for Efficient Computation of Path Centralities and Adaptive Routing in Large-scale Complex Commodity Networks

Publication Year: 2017, Page(s): 1
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Computing the most central nodes in large-scale commodity networks is rather important for improving routing and associated applications. In this paper, we introduce a novel framework for the analysis and efficient computation of routing path-based centrality measures, focusing on betweenness and traffic load centrality. The proposed framework enables efficient approximation and in special cases a... View full abstract»

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

Publication Year: 2014, Page(s):67 - 75
Cited by:  Papers (7)
| | PDF (218 KB) | HTML

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»

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

Publication Year: 2016, Page(s):106 - 116
| | PDF (390 KB) | HTML

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»

• ### Spreading Processes in Multilayer Networks

Publication Year: 2015, Page(s):65 - 83
Cited by:  Papers (20)
| | PDF (686 KB) | HTML

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»

• ### The Coevolution of Appraisal and Influence Networks Leads to Structural Balance

Publication Year: 2016, Page(s):286 - 298
Cited by:  Papers (1)
| | PDF (1449 KB) |  Media

In sociology, an appraisal structure, represented by a signed matrix or a signed network, describes an evaluative cognitive configuration among individuals. In this article we argue that interpersonal influences originate from positive interpersonal appraisals and, in turn, adjust individuals' appraisals of others. This mechanism amounts to a coevolution process of interpersonal appraisals and inf... View full abstract»

• ### Analyzing Vulnerability of Power Systems with Continuous Optimization Formulations

Publication Year: 2016, Page(s):132 - 146
| | PDF (1088 KB) | HTML

Potential vulnerabilities in a power grid can be exposed by identifying those transmission lines on which attacks (in the form of interference with their transmission capabilities) causes maximum disruption to the grid. In this study, we model the grid by (nonlinear) AC power flow equations, and assume that attacks take the form of increased impedance along transmission lines. We quantify disrupti... View full abstract»

• ### SIS Epidemic Spreading with Heterogeneous Infection Rates

Publication Year: 2017, Page(s): 1
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In this work, we aim to understand the influence of the heterogeneity of infection rates on the Susceptible-Infected-Susceptible (SIS) epidemic spreading. Employing the classic SIS model as the benchmark, we study the influence of the independently identically distributed infection rates on the average fraction of infected nodes in the metastable state. The log-normal, gamma and a newly designed d... 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 (21)
| | PDF (1164 KB) | HTML Media

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»

• ### Analyses of the Clustering Coefficient and the Pearson Degree Correlation Coefficient of Chung's Duplication Model

Publication Year: 2016, Page(s):117 - 131
| | PDF (698 KB) | HTML

Recent advances in gene expression profiling and proteomics techniques have spawn considerable interest in duplication models for modelling the evolution and growth of biological networks. In this paper, we consider the duplication model studied by Chung et al. It seems (to the best of our knowledge) that both the clustering coefficient and the Pearson degree correlation coefficient of this model ... 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 (1)
| | PDF (942 KB) | HTML

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»

• ### A Brief Survey of PageRank Algorithms

Publication Year: 2014, Page(s):38 - 42
Cited by:  Papers (1)
| | PDF (129 KB) | HTML

We examine several PageRank approximation algorithms. Quantitative analyses are provided to illustrate the extraordinary effectiveness of the PageRank computation. View full abstract»

• ### On Propagation of Phenomena in Interdependent Networks

Publication Year: 2016, Page(s):225 - 239
| | PDF (1459 KB)

When multiple networks are interconnected because of mutual service interdependence, propagation of phenomena across the networks is likely to occur. Depending on the type of networks and phenomenon, the propagation may be a desired effect, such as the spread of information or consensus in a social network, or an unwanted one, such as the propagation of a virus or a cascade of failures in a commun... View full abstract»

• ### Reciprocity and Efficiency in Peer Exchange of Wireless Nodes Through Convex Optimization

Publication Year: 2016, Page(s):257 - 270
| | PDF (681 KB) | HTML

This paper considers the allocation of exchange rates in a network of wireless nodes which engage in peer-to-peer dissemination. Here, in addition to the desirable throughput efficiency, it is important to ensure a level of rate reciprocity between peers, an issue that has been studied before only for wired networks. For the wireless substrate efficiency and reciprocity may be in conflict, due to ... View full abstract»

• ### Analysis of Centrality in Sublinear Preferential Attachment Trees via the Crump-Mode-Jagers Branching Process

Publication Year: 2017, Page(s):1 - 12
| | PDF (362 KB) | HTML

We investigate centrality and root-inference properties in a class of growing random graphs known as sublinear preferential attachment trees. We show that a continuous time branching processes called the Crump-Mode-Jagers (CMJ) branching process is well-suited to analyze such random trees, and prove that almost surely, a unique terminal tree centroid emerges, having the property that it becomes mo... View full abstract»

• ### Moment-Based Spectral Analysis of Random Graphs with Given Expected Degrees

Publication Year: 2017, Page(s): 1
| | PDF (768 KB)

We analyze the eigenvalues of a random graph ensemble, proposed by Chung and Lu, in which a given sequence of expected degrees, denoted by $\overline w_n=(w^{(n)}_1,\ldots,w^{(n)}_n)$ , is prescribed on the $n$ nodes of a random graph. We focus on the eigenvalues of the normalized (random) adjacency matrix of the graph ensemble, define... View full abstract»

• ### The Minimum Information Dominating Set for Opinion Sampling in Social Networks

Publication Year: 2016, Page(s):299 - 311
| | PDF (894 KB) | HTML

We consider the problem of sampling a node-valued graph. The objective is to infer the values of all nodes from that of a minimum subset of nodes by exploiting correlations in node values. We first introduce the concept of information dominating set (IDS). A subset of nodes in a given graph is an IDS if the values of these nodes are sufficient to infer the values of all nodes. We focus on two fund... View full abstract»

• ### Suppressing Epidemics in Networks Using Priority Planning

Publication Year: 2016, Page(s):271 - 285
| | PDF (906 KB) | HTML

In this paper, we analyze a large class of dynamic resource allocation (DRA) strategies, named priority planning, that aim to suppress SIS epidemics taking place in a network. This is performed by distributing treatments of limited efficiency to its infected nodes, according to a priority-order precomputed offline. Under this perspective, an efficient DRA strategy for a given network can be design... View full abstract»

• ### Inference of Hidden Social Power through Opinion Formation in Complex Networks

Publication Year: 2017, Page(s): 1
| | PDF (625 KB)

Social networks analysis and mining gets ever-increasing importance in various disciplines. In this context finding the most influential nodes with the highest social power on others is important in many applications including spreading of innovation, opinion formation, immunization, information propagation and recommendation. In this manuscript, we propose a mathematical framework in order to eff... View full abstract»

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

Publication Year: 2016, Page(s):17 - 31
| | PDF (981 KB) | HTML

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»

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

Publication Year: 2014, Page(s):23 - 37
Cited by:  Papers (4)
| | PDF (548 KB) | HTML

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)
| | PDF (443 KB) | HTML

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»

• ### Pattern Formation over Multigraphs

Publication Year: 2017, Page(s): 1
| | PDF (1145 KB) |  Media

Two of the most common pattern formation mechanisms are Turing-patterning in reaction-diffusion systems and lateral inhibition of neighboring cells. In this paper, we introduce a broad dynamical model of interconnected modules to study the emergence of patterns, with the above mentioned two mechanisms as special cases. Our results do not restrict the number of modules or their complexity, allow mu... 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 (3)
| | PDF (280 KB) | HTML

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»

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

Publication Year: 2016, Page(s):2 - 16
Cited by:  Papers (2)
| | PDF (593 KB) | HTML Media

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»

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

Publication Year: 2016, Page(s):44 - 57
Cited by:  Papers (9)
| | PDF (1906 KB) | HTML

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»

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

Publication Year: 2015, Page(s):40 - 52
Cited by:  Papers (1)
| | PDF (883 KB) | HTML

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»

• ### On Detection and Structural Reconstruction of Small-World Random Networks

Publication Year: 2017, Page(s): 1
| | PDF (4889 KB)

In this paper, we study detection and fast reconstruction of the celebrated Watts-Strogatz (WS) small-world random graph model (Watts and Strogatz, 1998) which aims to describe real-world complex networks that exhibit both high clustering and short average length properties. The WS model with neighborhood size k and rewiring probability probability can be viewed as a continuous interpolation betwe... View full abstract»

• ### Phase-Coupled Oscillators with Plastic Coupling: Synchronization and Stability

Publication Year: 2016, Page(s):240 - 256
| | PDF (671 KB)

In this article we study synchronization of systems of homogeneous phase-coupled oscillators with plastic coupling strengths and arbitrary underlying topology. The dynamics of the coupling strength between two oscillators is governed by the phase difference between these oscillators. We show that, under mild assumptions, such systems are gradient systems, and always achieve frequency synchronizati... View full abstract»

Publication Year: 2016, Page(s):183 - 196
| | PDF (1339 KB)

The problem of cascading failures in cyber-physical systems is drawing much attention in lieu of different network models for a diverse range of applications. While many analytic results have been reported for the case of large networks, very few of them are readily applicable to finite-size networks. This paper studies cascading failures in load-dependent finite-size geometric networks where the ... View full abstract»

• ### A Micro-foundation of Social Capital in Evolving Social Networks

Publication Year: 2017, Page(s): 1
| | PDF (2909 KB) |  Media

A social network confers benefits and advantages on individuals (and on groups); the literature refers to these advantages as social capital. This paper presents a micro-founded mathematical model of the evolution of a social network and of the social capital of individuals within the network. The evolution of the network is influenced by the extent to which individuals are homophilic, structurall... 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