| 1. |
- Como, Giacomo, et al.
(author)
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Robustness of Large-Scale Stochastic Matrices to Localized Perturbations
- 2015
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 2:2, s. 53-64
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Journal article (peer-reviewed)abstract
- 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 averaging algorithms for estimation or control. Hence, a central problem in network science and engineering is that of assessing the robustness of such invariant probability vectors to perturbations possibly localized on some relatively small part of the network. In this work, upper bounds are derived on the total variation distance between the invariant probability vectors of two stochastic matrices differing on a subset W of rows. Such bounds depend on three parameters: the mixing time and the entrance time on the set W for the Markov chain associated to one of the matrices; and the exit probability from the set W for the Markov chain associated to the other matrix. These results, obtained through coupling techniques, prove particularly useful in scenarios where W is a small subset of the state space, even if the difference between the two matrices is not small in any norm. Several applications to large-scale network problems are discussed, including robustness of Google's PageRank algorithm, distributed averaging, consensus algorithms, and the voter model.
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| 2. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Information Diffusion in Social Networks in Two Phases
- 2016
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 3:4, s. 197-210
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Journal article (peer-reviewed)abstract
- 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 effect of selecting and activating seed nodes in multiple phases. Specifically, we study diffusion in two phases assuming the well-studied independent cascade model. First, we formulate an objective function for two-phase diffusion, investigate its properties, and propose efficient algorithms for finding seed nodes in the two phases. Next, we study two associated problems: (1) budget splitting which seeks to optimally split the total budget between the two phases and (2) scheduling which seeks to determine an optimal delay after which to commence the second phase. Our main conclusions include: (a) under strict temporal constraints, use single phase diffusion, (b) under moderate temporal constraints, use two-phase diffusion with a short delay while allocating most of the budget to the first phase, and (c) when there are no temporal constraints, use two-phase diffusion with a long delay while allocating roughly one-third of the budget to the first phase.
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| 3. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Investment Strategies for Competing Camps in a Social Network: A Broad Framework
- 2019
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 6:4, s. 628-645
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Journal article (peer-reviewed)abstract
- We study the problem of optimally investing in nodes of a social network in a competitive setting, wherein two camps aim to drive the average opinion of the population in their own favor. Using a well-established model of opinion dynamics, we formulate the problem as a zero-sum game with its players being the two camps. We derive optimal investment strategies for both camps, and show that a random investment strategy is optimal when the underlying network follows a popular class of weight distributions. We study a broad framework, where we consider various well-motivated settings of the problem, namely, when the influence of a camp on a node is a concave function of its investment on that node, when a camp aims at maximizing competitor's investment or deviation from its desired investment, and when one of the camps has uncertain information about the values of the model parameters. We also study a Stackelberg variant of this game under common coupled constraints on the combined investments by the camps and derive their equilibrium strategies, and hence quantify the first-mover advantage. For a quantitative and illustrative study, we conduct simulations on real-world datasets and provide results and insights.
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| 4. |
- Dhamal, Swapnil Vilas, 1988, et al.
(author)
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Modeling Spread of Preferences in Social Networks for Sampling-based Preference Aggregation
- 2019
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 6:1, s. 46-59
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Journal article (peer-reviewed)abstract
- Given a large population, it is an intensive task to gather individual preferences over a set of alternatives and arrive at an aggregate or collective preference of the population. We show that social network underlying the population can be harnessed to accomplish this task effectively, by sampling preferences of a small subset of representative nodes. We first develop a Facebook app to create a dataset consisting of preferences of nodes and the underlying social network, using which, we develop models that capture how preferences are distributed among nodes in a typical social network. We hence propose an appropriate objective function for the problem of selecting best representative nodes. We devise two algorithms, namely, Greedy-min which provides a performance guarantee for a wide class of popular voting rules, and Greedy-sum which exhibits excellent performance in practice. We compare the performance of these proposed algorithms against random-polling and popular centrality measures, and provide a detailed analysis of the obtained results. Our analysis suggests that selecting representatives using social network information is advantageous for aggregating preferences related to personal topics (e.g., lifestyle), while random polling with a reasonable sample size is good enough for aggregating preferences related to social topics (e.g., government policies).
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| 5. |
- Giordano, Giulia, et al.
(author)
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The Smallest Eigenvalue of the Generalized Laplacian Matrix, with Application to Network-Decentralized Estimation for Homogeneous Systems
- 2016
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 3:4, s. 312-324
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Journal article (peer-reviewed)abstract
- 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 network-decentralized observer can be designed by starting from local observers (typically, optimal filters) and then adapting the gain to ensure overall stability. The smallest eigenvalue of the so-called generalized Laplacian matrix is crucial: stability is guaranteed if the gain is greater than the inverse of this eigenvalue, which is strictly positive if the graph is externally connected. To deal with uncertain topologies, we characterize the worst-case smallest eigenvalue of the generalized Laplacian matrix for externally connected graphs, and we prove that the worst-case graph is a chain. This general result provides a bound for the observer gain that ensures robustness of the network-decentralized observer even under arbitrary, possibly switching, configurations, and in the presence of noise.
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| 6. |
- Huang, Yuming, et al.
(author)
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Community Detection and Improved Detectability in Multiplex Networks
- 2020
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In: IEEE Transactions on Network Science and Engineering. - 2327-4697. ; 7:3, s. 1697-1709
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Journal article (peer-reviewed)abstract
- Belief propagation is a technique to optimize probabilistic graphical models, and has been used to solve the community detection problem for networks described by the stochastic block model. In this work, we investigate the community detection problem in multiplex networks with generic community label constraints using the belief propagation algorithm. Our main contribution is a generative model that does not assume consistent communities between layers and allows a potentially heterogeneous community structure, suitable in many real world multiplex networks, such as social networks. We show by numerical experiments that in the presence of consistent communities between different layers, consistent communities are matched, and the detectability is improved over single layers. We compare it with a "correlated model" which has the prior knowledge of community correlation between layers. Similar detectability improvement is obtained, even though our model has much milder assumptions than the "correlated model". When the network has heterogeneous community structures, our model is shown to yield a better detection performance over a certain parameter range.
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| 7. |
- Lakhan, Abdullah, et al.
(author)
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Blockchain-Enabled Cybersecurity Efficient IIOHT Cyber-Physical System for Medical Applications
- 2022
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In: IEEE Transactions on Network Science and Engineering. - Piscataway, NJ : IEEE. - 2327-4697 .- 2334-329X. ; 10:5, s. 2466-2479
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Journal article (peer-reviewed)abstract
- Cybersecurity issues such as malware, denial of service attacks, and unauthorized access to data for different applications are growing daily. The Industrial Internet of Healthcare Things (IIoHT) has recently been a new healthcare mechanism where many healthcare applications can run on hospital servers for remote medical services. For instance, cloud medical applications offer different services remotely from home. However, the existing IIoHT mechanisms can not handle critical cybersecurity issues and incur many medical care application processing and data security costs. The processing costs associated with security and deadline are the main findings of this proposed work. This work devises a cost-efficient blockchain task scheduling (CBTS) cyber-physical system (CPS) with different heuristics. All tasks are sorted, scheduled, and stored in a secure form in the IIoHT network. The performance evaluation proves that the CBTS framework outperforms the simulation results for the IIoHT application and reduces the cost by 50% of security execution and 33% of cybersecurity data validation blockchain costs compared to existing scheduling and blockchain schemes. © Copyright 2022 IEEE
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| 8. |
- Li, Jie, et al.
(author)
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Introduction to the Special Section on Big Data and Artificial Intelligence for Network Technologies
- 2020
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In: IEEE Transactions on Network Science and Engineering. - : IEEE. - 2327-4697. ; 7:1, s. 1-2
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Journal article (other academic/artistic)abstract
- The papers in this special section examines the deployment of Big Data and artificial intelligence for network technologies. The eneration of huge amounts of data, called big data, is creating the need for efficient tools to manage those data. Artificial intelligence (AI) has become the powerful tool in dealing with big data with recent breakthroughs at multiple fronts in machine learning, including deep learning. Meanwhile, information networks are becoming larger and more complicated, generating a huge amount of runtime statistics data such as traffic load, resource usages. The emerging big data and AI technologies may include a bunch of new requirements, applications and scenarios such as e-health, Intelligent Transportation Systems (ITS), Industrial Internet of Things (IIoT), and smart cities in the term of computing networks. The big data and AI driven network technologies also provide an unprecedented patient to discover new features, to characterize user demands and system capabilities in network resource assignment, security and privacy, system architecture, modeling and applications, which needs more explorations. The focus of this special section is to address the big data and artificial intelligence for network technologies. We appreciate contributions to this special section and the valuable and extensive efforts of the reviewers. The topics of this special section range from big data and AI algorithms, models, architecture for networks and systems to network architecture.
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| 9. |
- Liu, Dong, et al.
(author)
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Will Scale-Free Popularity Develop Scale-Free Geo-Social Networks?
- 2019
-
In: IEEE Transactions on Network Science and Engineering. - : Institute of Electrical and Electronics Engineers (IEEE). - 2327-4697. ; 6:3, s. 587-598
-
Journal article (peer-reviewed)abstract
- Empirical results show that spatial factors such as distance, population density and communication range affect our social activities, also reflected by the development of ties in social networks. This motivates the need for social network models that take these spatial factors into account. Therefore, in this paper we propose a gravity-low-based geo-social network model, where connections develop according to the popularity of the individuals, but are constrained through their geographic distance and the surrounding population density. Specifically, we consider a power-law distributed popularity, and random node positions governed by a Poisson point process. We evaluate the characteristics of the emerging networks, considering the degree distribution, the average degree of neighbors and the local clustering coefficient. These local metrics reflect the robustness of the network, the information dissemination speed and the communication locality. We show that unless the communication range is strictly limited, the emerging networks are scale-free, with a rank exponent affected by the spatial factors. Even the average neighbor degree and the local clustering coefficient show tendencies known in non-geographic scale-free networks, at least when considering individuals with low popularity. At high-popularity values, however, the spatial constraints lead to popularity-independent average neighbor degrees and clustering coefficients.
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| 10. |
- Liu, Yizhong, et al.
(author)
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A Blockchain-Based Cross-Domain Authentication Management System for IoT Devices
- 2024
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In: IEEE Transactions on Network Science and Engineering. - Piscataway, NJ : IEEE Computer Society. - 2327-4697. ; 11:1, s. 115-127
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Journal article (peer-reviewed)abstract
- With the emergence of the resource and equipment sharing concept, many enterprises and organizations begin to implement cross-domain sharing of devices, especially in the field of the Internet of Things (IoT). However, there are many problems in the cross-domain usage process of devices, such as access control, authentication, and privacy protection. In this paper, we make the following contributions. First, we propose a blockchain-based cross-domain authentication management system for IoT devices. The sensitive device information is stored in a Merkle tree structure where only the Merkle root is uploaded to the smart contract. Second, a detailed security and performance analysis is given. We prove that our system is secure against several potential security threats and satisfies validity and liveness. Compared to existing schemes, our schemes realize decentralization, privacy, scalability, fast off-chain authentication, and low on-chain storage. Third, we implement the system on Ethereum with varying parameters known as domain number, concurrent authentication request number, and Merkle tree leaf number. Experimental results show that our solution supports the management of millions of devices in a domain and can process more than 10,000 concurrent cross-domain authentication requests, consuming only 5531 ms. Meanwhile, the gas costs are shown to be acceptable. © IEEE
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