# 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

• ### A Mathematical Theory for Multistage Battery Switching Networks

Publication Year: 2018, Page(s):171 - 183
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In this paper, we propose a mathematical theory for multistage battery switching networks. The theory aims to address several design issues in managing a large-scale battery system, including flexibility, reliability, efficiency, complexity (scalability) and sustainability. Our multistage battery switching network is constructed by a concatenation of various rectangular “shapes” of battery packs. ... View full abstract»

• ### Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach

Publication Year: 2018, Page(s): 1
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Channel state information (CSI) estimation is one of the most fundamental problems in wireless communication systems. Various methods, so far, have been developed to conduct CSI estimation, which usually requires high computational complexity. However, these methods are not suitable for 5G wireless communications due to many techniques (e.g., massive MIMO, OFDM, and millimeter-Wave (mmWave)) to be... View full abstract»

• ### Finite-Time Passivity of Coupled Neural Networks with Multiple Weights

Publication Year: 2018, Page(s):184 - 197
Cited by:  Papers (1)
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This paper respectively studies finite-time passivity of multi-weighted coupled neural networks (MWCNNs) with and without coupling delays. First, based on those existing passivity definitions, several new concepts about finite-time passivity are presented. By exploiting these definitions of finite-time passivity and designing appropriate controllers, we investigate the passivity of MWCNNs with and... View full abstract»

• ### Deep Learning Meets Wireless Network Optimization: Identify Critical Links

Publication Year: 2018, Page(s): 1
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With the superior capability of discovering intricate structure of large data sets, deep learning has been widely applied in various areas including wireless networking. While existing deep learning applications mainly focus on data analysis, the role it can play on fundamental research issues in wireless networks is yet to be explored. With the proliferation of wireless networking infrastructure ... View full abstract»

• ### Isomorphisms in Multilayer Networks

Publication Year: 2018, Page(s):198 - 211
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We extend the concept of graph isomorphisms to multilayer networks with any number of “aspects” (i.e., types of layering). In developing this generalization, we identify multiple types of isomorphisms. For example, in multilayer networks with a single aspect, permuting vertex labels, layer labels, and both vertex labels and layer labels each yield different isomorphism relations between multilayer... View full abstract»

• ### Trust-based Social Networks with Computing, Caching and Communications: A Deep Reinforcement Learning Approach

Publication Year: 2018, Page(s): 1
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Social networks have continuously been expanding and trying to be innovative. The recent advances of computing, caching, and communication (3C) can have significant impacts on mobile social networks (MSNs). MSNs can leverage these new paradigms to provide a new mechanism for users to share resources (e.g., information, computation-based services). In this paper, we exploit the intrinsic nature of ... View full abstract»

• ### Provision of Public Goods on Networks: On Existence, Uniqueness, and Centralities

Publication Year: 2018, Page(s):225 - 236
Cited by:  Papers (1)
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We consider the provision of public goods on networks of strategic agents. We study different effort outcomes of these network games, namely, the Nash equilibria, Pareto efficient effort profiles, and semi-cooperative equilibria (resulting from interactions among coalitions of agents). We identify necessary and sufficient conditions on the structure of the network for the uniqueness of the Nash eq... View full abstract»

• ### Particle swarm optimization with moving particles on scale-free networks

Publication Year: 2018, Page(s): 1
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PSO is a nature-inspired optimization algorithm widely applied in many fields. In this paper, we present a variant named MP-PSO, in which some particles are allowed to move on a scale-free network and change the interaction pattern during the search course. In contrast to traditional PSOs with fixed interaction sources, MP-PSO shows better flexibility and diversity, where the structure of the part... View full abstract»

• ### Observational Equivalence in System Estimation: Contractions in Complex Networks

Publication Year: 2018, Page(s):212 - 224
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Observability of complex systems/networks is the focus of this paper, which is shown to be closely related to the concept of contraction. Indeed, for observable network tracking it is necessary/sufficient to have one node in each contraction measured. Therefore, nodes in a contraction are equivalent to recover for loss of observability, implying that contraction size is a key factor for observabil... View full abstract»

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

Publication Year: 2018, Page(s):2 - 13
Cited by:  Papers (1)
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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»

• ### Task Allocation Scheme for Cyber Physical Social Systems

Publication Year: 2018, Page(s): 1
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Cyber-physical social system (CPSS) has emerged to integrate the interaction between the physical, cyber, and social world. However, due to the ever-increasing number of sensing data and the limited resources of mobile systems, how to allocate the tasks by crowd sensing to enable a high-confidence CPSS becomes a new challenge. Therefore, in this paper we propose a novel game theoretic approach to ... View full abstract»

• ### Weighted Bearing-Compass Dynamics: Edge and Leader Selection

Publication Year: 2018, Page(s):247 - 260
Cited by:  Papers (1)
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This paper considers the design and effective interfaces of a distributed robotic formation running planar weighted bearing-compass dynamics. We present results that support methodologies to construct formation topologies using submodular optimization techniques. Further, a convex optimization framework is developed for the selection of edge weights which increase performance. We explore a method ... View full abstract»

• ### Towards Workload Balancing in Fog Computing Empowered IoT

Publication Year: 2018, Page(s): 1
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As latency is the key performance metric for IoT applications, fog nodes co-located with cellular base stations can move the computing resources close to IoT devices. Therefore, data flows of IoT devices can be offloaded to fog nodes in their proximity, instead of the remote cloud, for processing. However, the latency of data flows in IoT devices consist of both the communications latency and comp... View full abstract»

• ### Spreading Processes in Multilayer Networks

Publication Year: 2015, Page(s):65 - 83
Cited by:  Papers (58)
| | 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»

• ### Modeling and Performance Assessment of Dynamic Rate Adaptation for M2M Communications

Publication Year: 2018, Page(s): 1
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With the advance of Internet of Things and the support of a diverse array of smart-world applications, the number of Machine-to-Machine (M2M) devices has continued to grow at an accelerated rate. This significant and unchecked increase poses enormous challenges to both M2M infrastructure and the coexistence of M2M applications. In this paper, we model the traffic arrival patterns of time-driven, e... View full abstract»

• ### Recovering Asymmetric Communities in the Stochastic Block Model

Publication Year: 2018, Page(s):237 - 246
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We consider the sparse stochastic block model in the case where the degrees are uninformative. The case where the two communities have approximately the same size has been extensively studied and we concentrate here on the community detection problem in the case of unbalanced communities. In this setting, spectral algorithms based on the non-backtracking matrix are known to solve the community det... View full abstract»

• ### Robustness of Consensus over Weighted Digraphs

Publication Year: 2018, Page(s): 1
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This paper investigates the robustness of consensus protocols over weighted directed graphs using the Nyquist criterion and small gain theorem for agents with single and double integrator dynamics, respectively. For single integrators, the linear consensus protocol, described by the weighted Laplacian, is considered, while for double integrators a new consensus protocol is presented which also use... View full abstract»

• ### Cascading Failures in Interdependent Systems: Impact of Degree Variability and Dependence

Publication Year: 2018, Page(s):127 - 140
Cited by:  Papers (2)
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We study cascading failures in a system comprising interdependent networks/systems, in which nodes rely on other nodes both in the same system and in other systems to perform their function. The (inter-)dependence among nodes is modeled using a dependence graph, where the degree vector of a node determines the number of other nodes it can potentially cause to fail in each system through aforementi... View full abstract»

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

Publication Year: 2017, Page(s):13 - 26
Cited by:  Papers (2)
| | 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 Multi-Objective Evolutionary Algorithm for Promoting the Emergence of Cooperation and Controllable Robustness on Directed Networks

Publication Year: 2018, Page(s):92 - 100
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The directedness of links is of significance in complex networks, and much attention has been paid to study the dynamics of directed networks recently.In networked systems, where the emergence of cooperation and robustness are two hot issues in recent decades. Previous studies have indicated that the structures for promoting these two properties are opposite, which also reveals the great impact of... View full abstract»

• ### Adaptive and Fault-tolerant Data Processing in Healthcare IoT Based on Fog Computing

Publication Year: 2018, Page(s): 1
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In recent years, healthcare IoT have been helpful in mitigating pressures of hospital and medical resources caused by aging population to a large extent. As a safety-critical system, the rapid response from the health care system is extremely important. To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds. However... View full abstract»

• ### Neuronal spatial arrangement shapes effective connectivity traits of in vitro cortical networks

Publication Year: 2018, Page(s): 1
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We studied effective connectivity in rat cortical cultures with various degrees of spatial aggregation, ranging from homogeneous networks to highly aggregated ones. We considered small cultures 3 mm in diameter and that contained about 2000 neurons. Spatial inhomogeneity favored an increase of metric correlations and connectivity among neighboring neurons. Effective connectivity was determined fro... View full abstract»

• ### Incorporating Latent Constraints to Enhance Inference of Network Structure

Publication Year: 2018, Page(s): 1
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A complex network is a model representation of interactions within technological, social, information, and biological networks. Oftentimes, we are interested in identifying the underlying network structure from limited and noisy observational data, which is a challenging problem. Here, to address this problem, we propose a novel and effective technique that incorporates latent structural constrain... View full abstract»

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

Publication Year: 2018, Page(s):65 - 78
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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»

• ### 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»

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

Publication Year: 2016, Page(s):147 - 158
Cited by:  Papers (2)
| | 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»

• ### Algebraic Connectivity Under Site Percolation in Finite Weighted Graphs

Publication Year: 2018, Page(s):86 - 91
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We study the behavior of 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 connectivit... 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»

• ### Efficient Resource Allocation Utilizing Q-Learning in Multiple UA Communications

Publication Year: 2018, Page(s): 1
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In recent years, unmanned aircraft systems (UASs) have garnered significant attention, and the demand for communication utilizing unmanned aircrafts (UAs) has increased. However, the limited available frequency for UA communication, despite the increasing utilization, poses a problem. Moreover, for the practical application of UA communication, the changes and differences in the propagation enviro... View full abstract»

• ### Channel Selective Activity Recognition with WiFi: A Deep Learning Approach Exploring Wideband Information

Publication Year: 2018, Page(s): 1
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WiFi-based human activity recognition explores the correlations between body movement and the reflected WiFi signals to classify different activities. State-of-the-art solutions mostly work on a single WiFi channel and hence are quite sensitive to the quality of a particular channel. Co-channel interference in an indoor environment can seriously undermine the recognition accuracy. In this paper, w... View full abstract»

• ### A Reputation-Based Model for Trust Evaluation in Social Cyber-Physical Systems

Publication Year: 2018, Page(s): 1
| | PDF (744 KB)

The concept of social networking is integrated into the Cyber-Physical Systems (CPSs) for the purpose of allowing smart objects to establish social relationships in an autonomous way. Solutions based on social relationships are considered to be promising approaches for resource/service discovery. The primary issues in a social-CPS (SCPS) involve how to define, quantify and eventually establish soc... View full abstract»

• ### Deep Convolutional Neural Networks for Indoor Localization with CSI Images

Publication Year: 2018, Page(s): 1
| | PDF (1091 KB)

With the increasing demand of location-based services, Wi-Fi based localization has attracted great interest because it provides ubiquitous access in indoor environments. In this paper, we propose CiFi, deep convolutional neural networks (DCNN) for indoor localization with commodity 5GHz WiFi. First, by leveraging a modified device driver, we can extract phase data of channel state information (CS... View full abstract»

• ### Convergence Analysis of a Distributed Optimization Algorithm with a General Unbalanced Directed Communication Network

Publication Year: 2018, Page(s): 1
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In this paper, we discuss a class of distributed constrained optimization problems in power systems where the target is to optimize the sum of all agents&#x0027; local convex objective functions over a general unbalanced directed communication network. Each local convex objective function is known exclusively to a single agent, and the agents&#x0027; variables are constrained to global cou... View full abstract»

• ### Reliability Analysis of IoT Networks with Community Structures

Publication Year: 2018, Page(s): 1
| | PDF (506 KB)

Network infrastructure and connectivity in the Internet of Things (IoT) applications are becoming increasingly complex and heterogeneous, opening up many challenges including reliability. Many real-world networks exhibit community structure, where the networked devices can be easily grouped into sets with dense internal connections but sparse connections between different sets. Examples of such co... View full abstract»

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

Publication Year: 2018, Page(s):42 - 54
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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»

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

Publication Year: 2014, Page(s):76 - 90
Cited by:  Papers (51)
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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»

• ### Analysis of Partial Diffusion LMS for Adaptive Estimation Over Networks with Noisy Links

Publication Year: 2018, Page(s):101 - 112
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In partial diffusion-based least mean square (PDLMS) scheme, each node shares a part of its intermediate estimate vector with its neighbors at each iteration. In this paper, besides studying the general PDLMS scheme, we figure out how the noisy links deteriorate the network performance during the exchange of weight estimates. We investigate the steady state mean square deviation (MSD) and derive a... View full abstract»

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

Publication Year: 2015, Page(s):164 - 178
Cited by:  Papers (6)
| | PDF (425 KB) | HTML

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»

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

Publication Year: 2018, Page(s): 1
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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»

• ### A Global Optimization Approach Based on Opinion Formation in Complex Networks

Publication Year: 2018, Page(s): 1
| | PDF (8795 KB)

Population-based metaheuristic optimization techniques have numerous applications in science and engineering. In this paper, we introduce a novel population-based binary optimization method, built upon consensus formation in interacting multi-agent systems. Agents, each associated with an opinion vector, are linked together through a network structure. The agents influence each other by performing... 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»

• ### Caching for Mobile Social Networks with Deep Learning: Twitter Analysis for 2016 U.S. Election

Publication Year: 2018, Page(s): 1
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As the rise of the portable devices, people usually access the social media such as Twitter and Facebook through wireless networks. Therefore, data transmission rates significant important to the end users. In this work, we discuss the problem of context-aware data caching in the heterogeneous small cell networks to reduce the service delay and how the device-to-device (D2D) and device-to-infrastr... View full abstract»

• ### A Brief Survey of PageRank Algorithms

Publication Year: 2014, Page(s):38 - 42
Cited by:  Papers (11)
| | 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»

• ### Henneberg Growth of Social Networks: Modeling the Facebook

Publication Year: 2018, Page(s): 1
| | PDF (17627 KB)

Social networks are complex in their forming and growing processes. Tremendous empirical evidence in undirected social networks, such as Facebook, Quora and Foursquare, demonstrates that, to a large extent, individuals are associated with each other not by preference but through other organizing rules. One such rule found in many real social networks is the Henneberg growth mechanism, with which a... View full abstract»

• ### A Two-stage Stochastic Optimization Approach for Detecting Structurally Stable Clusters in Randomly Changing Graphs

Publication Year: 2018, Page(s): 1
| | PDF (629 KB)

We introduce a stochastic extension of the problem of finding nonhereditary subgraphs of maximum size in randomly changing graphs. The proposed formulation utilizes a two-stage stochastic optimization framework for identifying subgraphs whose structural properties can be preserved and repaired whenever the underlying graph's topology changes randomly. Particular focus is placed on finding nonhered... View full abstract»

• ### Multi-Scale Factor Analysis of High-Dimensional Functional Connectivity in Brain Networks

Publication Year: 2018, Page(s): 1
| | PDF (2576 KB) |  Media

We consider challenges in modeling and estimating high-dimensional functional connectivity in brain networks with a large number of nodes arranged in a hierarchical and modular structure. We develop a multi-scale factor analysis (MSFA) model which partitions the massive neuroimaging time series data defined over the brain networks into a finite set of regional clusters. To achieve further dimensio... View full abstract»

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

Publication Year: 2017, Page(s):201 - 214
Cited by:  Papers (2)
| | PDF (350 KB) | HTML

The$k$-complex contagionmodel is a social contagion model which describes the diffusion of behaviors in networks where the successful adoption of a behavior requires influence from... View full abstract»

• ### QTCP: Adaptive Congestion Control with Reinforcement Learning

Publication Year: 2018, Page(s): 1
| | PDF (4015 KB)

Next generation network access technologies and Internet applications have increased the challenge of providing satisfactory quality of experience for users with traditional congestion control protocols. Efforts on optimizing the performance of TCP by modifying the core congestion control method depending on specific network architectures or apps do not generalize well under a wide range of networ... View full abstract»

• ### Efficient Privacy-preserving Machine Learning in Hierarchical Distributed System

Publication Year: 2018, Page(s): 1
| | PDF (2901 KB)

With the dramatic growth of data in both amount and scale, distributed machine learning has become an important tool to discover the essential knowledge from massive data. However, it is infeasible to aggregate data from all data owners due to the practical physical constraints. Potential privacy leakage during distributed machine learning also deters participants to share their raw data. To tackl... View full abstract»

• ### A Robust Advantaged Node Placement Strategy for Sparse Network Graphs

Publication Year: 2018, Page(s):113 - 126
| | PDF (1159 KB) | HTML

Establishing robust connectivity in heterogeneous networks (HetNets) is an important yet challenging problem. For a HetNet accommodating a large number of nodes, establishing perturbation-invulnerable connectivity is of utmost importance. This paper provides a robust advantaged node placement strategy best suited for sparse network graphs. In order to offer connectivity robustness, this paper mode... 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

GEditor-in-Chief
Dapeng Oliver Wu

University of Florida

Dept. of Electrical  &  Computer Engineering

P. O. Box 116130

Gainesville, FL 32611

Email: dpwu@ufl.edu