# IEEE Transactions on Network Science and Engineering

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• ### Renewable Energy-Aware Big Data Analytics in Geo-distributed Data Centers with Reinforcement Learning

Publication Year: 2018, Page(s): 1
| | PDF (830 KB)

In the age of big data, companies tend to deploy their services in data centers rather than their own servers. The demands of big data analytics grow significantly, which leads to an extremely high electricity consumption at data centers. In this paper, we investigate the cost minimization problem of big data analytics on geo-distributed data centers connected to renewable energy sources with unpr... View full abstract»

• ### Pattern Formation over Multigraphs

Publication Year: 2018, Page(s):55 - 64
| | PDF (869 KB) | HTML 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»

• ### Scheduling Resource-Bounded Monitoring Devices for Event Detection and Isolation in Networks

Publication Year: 2018, Page(s):65 - 78
| | PDF (1048 KB) | HTML

In networked systems, monitoring devices such as sensors are typically deployed to monitor various target locations. Targets are the points in the physical space at which events of some interest, such as random faults or attacks, can occur. Most often, monitoring devices have limited energy supplies, and they can operate for a limited duration. As a result, energy-efficient monitoring of target lo... View full abstract»

• ### Information Flow in a Model of Policy Diffusion: An Analytical Study

Publication Year: 2018, Page(s):42 - 54
| | PDF (948 KB) | HTML

Networks are pervasive across science and engineering, but seldom do we precisely know their topology. The information-theoretic notion of transfer entropy has been recently proposed as a potent means to unveil connectivity patterns underlying collective dynamics of complex systems. By pairwise comparing time series of units in the network, transfer entropy promises to determine whether the units ... View full abstract»

• ### Mitigating Bottlenecks in Wide Area Data Analytics via Machine Learning

Publication Year: 2018, Page(s): 1
| | PDF (1321 KB)

Over the past decade, we have witnessed exponential growth in the density (petabyte-level) and breadth (across geo-distributed datacenters) of data distribution. It becomes increasingly challenging but imperative to minimize the response times of data analytic queries over multiple geo-distributed datacenters. However, existing scheduling-based solutions have largely been motivated by pre-establis... View full abstract»

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

Publication Year: 2017, Page(s):248 - 260
| | PDF (1373 KB) | HTML

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»

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

Publication Year: 2017, Page(s):154 - 164
| | PDF (472 KB) | HTML

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 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 effectively e... View full abstract»

• ### Tracking Network Evolution and Their Applications in Structural Network Analysis

Publication Year: 2018, Page(s): 1
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Structural network analysis, including node ranking, community detection and link prediction, has received a lot of attention lately. In the literature, most works focused on structural analysis of a single network. In this paper, we are particularly interested in how the network structure evolves over time. For this, we propose a general framework to track, model, and predict the dynamic network ... View full abstract»

• ### A Bi-Virus Competing Spreading Model with Generic Infection Rates

Publication Year: 2018, Page(s):2 - 13
| | PDF (1246 KB) | HTML Media

Due to widespread applications, the multi-virus competing spreading dynamics has recently aroused considerable interests. To our knowledge, all previous competing spreading models assume infection rates that are each linear in the virus occupancy probabilities of the individuals in a population. As linear infection rates are overestimation of real infection rates, in some situations these models c... View full abstract»

• ### Image and Attribute Based Convolutional Neural Network Inference Attacks in Social Networks

Publication Year: 2018, Page(s): 1
| | PDF (3978 KB)

In modern society, social networks play an important role for online users. However, one unignorable problem behind the booming of the services is privacy issues. At the same time, neural networks have been swiftly developed in recent years, and are proven to be very effective in inference attacks. This paper proposes a new framework for inference attacks in social networks, which smartly integrat... View full abstract»

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

Publication Year: 2018, Page(s):14 - 31
| | PDF (3055 KB) | HTML Media

A social network confers benefits and advantages on individuals (and on groups); the literature refers to these benefits and advantages as social capital. An individual's social capital depends on its position in the network and on the shape of the network-but positions in the network and the shape of the network are determined endogenously and change as the network forms and evolves. This paper p... View full abstract»

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

Publication Year: 2017, Page(s):215 - 228
| | PDF (505 KB) | HTML

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 View full abstract»

• ### Network Maximal Correlation

Publication Year: 2017, Page(s):229 - 247
| | PDF (1310 KB) | HTML

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»

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

Publication Year: 2016, Page(s):147 - 158
Cited by:  Papers (1)
| | PDF (483 KB) | HTML Media

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»

• ### Graph Theoretical Analysis on Distributed Line Graphs for Peer-to-Peer Networks

Publication Year: 2018, Page(s): 1
| | PDF (3889 KB)

Distributed line graphs were introduced by Zhang and Liu as an overlay for Peer-to-Peer networks. Distributed line graphs have some useful properties as a network topology, such as out-regular and small diameter, where the former implies that each user possesses a constant size of routing table and the latter means that a reasonably small number of hops is necessary to reach a target user. In this... View full abstract»

• ### Node Scheduling and Compressed Sampling for Event Reporting in WSNs

Publication Year: 2018, Page(s): 1
| | PDF (9535 KB)

This work focuses on developing a node scheduling algorithm for detecting events in a sensor field such that few random samples from a set of the active sensor nodes are transmitted to the cluster head and are further used for almost complete reconstruction of the cluster data. A node scheduling algorithm is proposed to achieve maximum coverage of the physical sensor field with correlated sensor r... View full abstract»

• ### Structure-based Sybil Detection in Social Networks via Local Rule-based Propagation

Publication Year: 2018, Page(s): 1
| | PDF (828 KB)

Social networks are known to be vulnerable to Sybil attack, in which an attacker maintains massive Sybils to perform various malicious activities. Therefore, Sybil detection in social networks is a fundamental security research problem. Structure-based methods have been shown to be promising at detecting Sybils. Existing structure-based methods can be classified into Random Walk (RW)-based methods... View full abstract»

• ### Identifying Influential Spreaders in Complex Multilayer Networks: A centrality perspective

Publication Year: 2017, Page(s): 1
| | PDF (251 KB) |  Media

Identifying influential spreaders in complex networks is of paramount importance for understanding and controlling the spreading dynamics. A challenging and yet inadequately explored task is to detect such influential nodes in multilayer networks, i.e., networks that encompass different types of connections (e.g., different relationships) among the nodes, hence facilitating a multilayer structure.... View full abstract»

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

Publication Year: 2017, Page(s):201 - 214
| | PDF (350 KB) | HTML

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 t... View full abstract»

• ### Spreading Processes in Multilayer Networks

Publication Year: 2015, Page(s):65 - 83
Cited by:  Papers (29)
| | 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»

• ### Scalable Privacy-Preserving Participant Selection for Mobile Crowdsensing Systems: Participant Grouping and Secure Group Bidding

Publication Year: 2018, Page(s): 1
| | PDF (1739 KB)

Mobile crowdsensing (MCS) has been emerging as a new sensing paradigm where vast numbers of mobile devices are used for sensing and collecting data in various applications. Auction based participant selection has been widely used for current MCS systems to achieve user incentive and task assignment optimization. However, participant selection problems solved with auction-based approaches usually i... View full abstract»

• ### SIS Epidemic Spreading with Heterogeneous Infection Rates

Publication Year: 2017, Page(s):177 - 186
| | PDF (828 KB) | HTML

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»

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

Publication Year: 2017, Page(s):13 - 26
| | PDF (611 KB) | HTML Media

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»

• ### A Resource Allocation Mechanism for Cloud Radio Access Network Based on Cell Differentiation And Integration Concept

Publication Year: 2017, Page(s): 1
| | PDF (3456 KB)

A Self-Organising Cloud Radio Access Network (C-RAN) is proposed, which dynamically adapt to varying capacity demands. The Base Band Units and Remote Radio Heads are scaled semi-statically based on the concept of cell differentiation and integration (CDI) while a dynamic load balancing is formulated as an integer-based optimisation problem with constraints. A Discrete Particle Swarm Optimisation (... View full abstract»

• ### Contract Mechanism and Performance Analysis for Data Transaction in Mobile Social Networks

Publication Year: 2017, Page(s): 1
| | PDF (1062 KB)

In this paper, a novel mobile data offloading method is proposed based on an external-infrastructure-free approach. Specifically, through the hotspot function of smartphones, data demands from some mobile users, who have used out the data with their monthly data plans, can be offloaded by some other mobile users who still have redundant unused data. In order to model this data transaction among mo... View full abstract»

• ### Incompatibility Boundaries for Properties of Community Partitions

Publication Year: 2018, Page(s):32 - 41
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We prove the incompatibility of certain desirable properties of community partition quality functions. Our results generalize the impossibility result of [Kleinberg 2003] by considering sets of weaker properties. In particular, we use an alternative notion to solve the central issue of the consistency property. (The latter means that modifying the graph in a way consistent with a partition should ... 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):140 - 153
| | PDF (703 KB) | HTML

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»

Publication Year: 2018, Page(s): 1
| | PDF (1448 KB)

This paper studies a promising application in Vehicular Cyber-Physical Systems (VCPS) called roadside advertisement dissemination. Its application involves three elements: the drivers in the vehicles, Roadside Access Points (RAPs), and shopkeepers. The shopkeeper wants to attract as many customers as possible by using RAPs to disseminate advertisements to the passing vehicles. Upon receiving an ad... View full abstract»

• ### Scheduling Jobs across Geo-Distributed Datacenters with Max-Min Fairness

Publication Year: 2018, Page(s): 1
| | PDF (4599 KB)

It has become routine for large volumes of data to be generated, stored, and processed across geographically distributed datacenters. To run a single data analytic job on such geo-distributed data, recent research proposed to distribute its tasks across datacenters, considering both data locality and network bandwidth across datacenters. Yet, it remains an open problem in the more general case, wh... View full abstract»

• ### A Brief Survey of PageRank Algorithms

Publication Year: 2014, Page(s):38 - 42
Cited by:  Papers (3)
| | 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»

• ### Information Propagation in Clustered Multilayer Networks

Publication Year: 2016, Page(s):211 - 224
Cited by:  Papers (4)
| | 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»

• ### Diffusion of Innovation in Large Scale Graphs

Publication Year: 2017, Page(s):100 - 111
| | PDF (439 KB) | HTML

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»

• ### Distributed Link Removal Using Local Estimation of Network Topology

Publication Year: 2018, Page(s): 1
| | PDF (1011 KB) |  Media

This paper considers the problem of network structure manipulation in the absence of information on the global network topology. In particular, the problem of removing some of links is investigated in order to slow or stop the spread of disease in a network while preserving its connectivity. Existing methods solve this combinatorial problem in a centralized manner and they require the global infor... View full abstract»

• ### Fast Generation of Spatially Embedded Random Networks

Publication Year: 2017, Page(s):112 - 119
| | PDF (557 KB) | HTML

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»

• ### Controlling Best Response Dynamics for Network Games

Publication Year: 2018, Page(s): 1
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Controlling networked dynamical systems is a complex endeavor, specifically keeping in mind the fact that in many scenarios the actors that are engaged in the dynamism behave selfishly and therefore only take into account their own individual utility, a setting that has been widely studied in the field of game theory. One way that we can control the system dynamics is through the use of control pa... View full abstract»

• ### A Fair Mechanism for Private Data Publication in Online Social Networks

Publication Year: 2018, Page(s): 1
| | PDF (8314 KB)

Due to the tremendous growth of online social networks in both participants and collected contents, social data publication has provided an opportunity for numerous services. However, neglectfully publishing all the contents leads to severe disclosure of sensitive information due to diverse user behaviors. Therefore, there should be a thoroughly designed framework for data publication in online so... View full abstract»

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

Publication Year: 2017, Page(s):120 - 128
Cited by:  Papers (1)
| | 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»

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

Publication Year: 2017, Page(s):165 - 176
| | PDF (405 KB) | HTML

In this paper, we study detection and fast reconstruction of the celebrated Watts-Strogatz (WS) small-world random graph model [29] 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 β can be viewed as a continuous interpolation between a determin... View full abstract»

• ### Minimizing Social Cost of Vaccinating Network SIS Epidemics

Publication Year: 2017, Page(s): 1
| | PDF (2088 KB)

Reducing the economic costs (losses) as much as possible is one of the main goals of controlling virus spreading and worm propagation on complex networks. Taking into account the interactions and conflicts of interests among egoistic individuals (nodes) in a network, we introduce the zero-determinant (ZD) strategy into our proposed non-cooperative networking vaccination game with the economic ince... 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»

• ### Pricing for profit in Internet of Things

Publication Year: 2018, Page(s): 1
| | PDF (4331 KB) |  Media

The economic model of the Internet of Things (IoT) consists of end users, advertisers and three different kinds of providers-IoT service provider (IoTSP), Wireless service provider (WSP) and cloud service provider (CSP). We investigate three different kinds of interactions among the providers. First, we consider that the IoTSP prices a bundled service to the end-users, and the WSP and CSP pay the ... 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
Cited by:  Papers (2)
| | PDF (3593 KB) | HTML

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»

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

Publication Year: 2017, Page(s):75 - 82
| | PDF (571 KB) | HTML

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»

Publication Year: 2017, Page(s):129 - 139
| | PDF (696 KB) | HTML Media

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»

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

Publication Year: 2015, Page(s):127 - 138
Cited by:  Papers (2)
| | PDF (453 KB) | HTML

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»

• ### Contact Adaption during Epidemics: A Multilayer Network Formulation Approach

Publication Year: 2017, Page(s): 1
| | PDF (6956 KB) |  Media

People change their physical contacts as a preventive response to infectious disease propagations. Yet, only a few mathematical models consider the coupled dynamics of the disease propagation and the contact adaptation process. This paper presents a model where each agent has a default contact neighborhood set, and switches to a different contact set once she becomes alert about infection among he... View full abstract»

• ### Passivity Analysis and Pinning Control of Multi-Weighted Complex Dynamical Networks

Publication Year: 2017, Page(s): 1
| | PDF (3612 KB)

This paper studies a multi-weighted network model with different dimensions of output and input vectors. Firstly, we analyze passivity of proposed network model by employing some inequality techniques and Lyapunov functional method, and give a synchronization condition for output-strictly passive complex dynamical network with multi-weights (CDNMWs). Furthermore, by using pinning adaptive strategi... 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)
| | 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»

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

Publication Year: 2016, Page(s):95 - 105
Cited by:  Papers (11)
| | 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»

• ### An Efficient Randomized Algorithm for Rumor Blocking in Online Social Networks

Publication Year: 2017, Page(s): 1
| | PDF (940 KB) |  Media

Social networks allow rapid spread of ideas and innovations while negative information can also propagate widely. When a user receives two opposing opinions, they tend to believe the one arrives first. Therefore, once misinformation or rumor is detected, one containment method is to introduce a positive cascade competing against the rumor. Given a budget $k$, the rumo... 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