Averting Cascading Failures in Networked Infrastructures: Poset-Constrained Graph Algorithms

Yu, Pei-Duo; Tan, Chee Wei; Fu, Hung‐Lin

doi:10.1109/jstsp.2018.2844813

Cited by 20 publications

(23 citation statements)

References 36 publications

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“…u is a tree, we can apply the result of Theorem 3 in [6] to complete the rest of the proof. Corollary 2.…”

Section: Combining Two Results Above We Havementioning

confidence: 99%

“…The authors in [5] then proved that the distance center of the rumor subgraph was shown to be a good approximation to the rumor source. In particular, the distance center is exactly the maximum-likelihood estimator of the rumor source when the rumor subgraph is a degree regular tree [6]. Thus, we focus on utilizing MEGA to find the distance-based rumor source estimator.…”

Section: Preliminaries Of Network Centrality Computationmentioning

confidence: 99%

“…The evolution of an infodemic can be modeled as dynamic processes on large-scale networks, and statistical network analysis is therefore playing an important role in infodemic control. Examples of such control measures are network motif enumeration for community detection [3, 4] or rumor source detection [5, 6] in online social networks. Online social networks such as Facebook that boasts of over 2.7 billion monthly active users may want to identify important communities or count user clusters efficiently.…”

Section: Introductionmentioning

confidence: 99%

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MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

Hang

Ling³

et al. 2020

Preprint

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Statistical network analysis plays a critical role in managing the coronavirus disease (COVID-19) infodemic such as addressing community detection and rumor source detection problems in social networks. As the data underlying infodemiology are fundamentally huge graphs and statistical in nature, there are computational challenges to the design of graph algorithms and algorithmic speedup. A framework that leverages cloud computing is key to designing scalable data analytics for infodemic control. This paper proposes the MEGA framework, which is a novel joint hierarchical clustering and parallel computing technique that can be used to process a variety of computational tasks in large graphs. Its unique feature lies in using statistical machine learning to exploit the inherent statistics of data to accelerate computation. Our MEGA framework consists of first pruning, followed by hierarchical clustering based on geodesic distance and then parallel computing, lending itself readily to parallel computing software, e.g., MapReduce or Hadoop. In particular, we illustrate how our MEGA framework computes two representative graph problems for infodemic control, namely network motif counting for community detection and network centrality computation for rumor source detection. Interesting special cases of optimal tuning in the MEGA framework are identified based on geodesic distance characterization and random graph model analysis. Finally, we evaluate its performance using cloud software implementation and real-world graph datasets to demonstrate its computational efficiency over existing state of the art.

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“…u is a tree, we can apply the result of Theorem 3 in [6] to complete the rest of the proof. Corollary 2.…”

Section: Combining Two Results Above We Havementioning

confidence: 99%

Section: Preliminaries Of Network Centrality Computationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

Hang

Ling³

et al. 2020

Preprint

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“…The work in [16] considered the Susceptible-Infected-Recovered spreading model and proposed a sample-path-based approach to finding the source estimator. Other similar problem variants include the work in [17] on obfuscating the source instead of identifying it, the work in [18] on protecting the network in advance to minimize the impact of cascading failure, and the work in [19] consider a dynamic network model where edges may vary over time.…”

Section: Introductionmentioning

confidence: 99%

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

Yu,

Tan,

2022

Preprint

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We study the epidemic source detection problem in contact tracing networks modeled as a graph-constrained maximum likelihood estimation problem using the susceptible-infected model in epidemiology. Based on a snapshot observation of the infection subgraph, we first study finite degree regular graphs and regular graphs with cycles separately, thereby establishing a mathematical equivalence in maximal likelihood ratio between the case of finite acyclic graphs and that of cyclic graphs. In particular, we show that the optimal solution of the maximum likelihood estimator can be refined to distances on graphs based on a novel statistical distance centrality that captures the optimality of the nonconvex problem. An efficient contact tracing algorithm is then proposed to solve the general case of finite degree-regular graphs with multiple cycles. Our performance evaluation on a variety of graphs shows that our algorithms outperform the existing state-of-the-art heuristics using contact tracing data from the SARS-CoV 2003 and COVID-19 pandemics by correctly identifying the superspreaders on some of the largest superspreading infection clusters in Singapore and Taiwan.

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“…In order to spread D2D-based SNS as a future Internet application, it is indispensable to avoid the cascading failure. Therefore, several methods have already been proposed [18], [19]. However, these methods are not so effective in D2D-based SNS because the complete information on network topology is needed.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Relay Device Placement Algorithm for Avoiding Cascading Failure in D2D-Based Social Networking Service

Yokoi

Tachibana

2021

IEICE Trans. Inf. & Syst.

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In this paper, in order to avoid the cascading failure by increasing the number of links in the physical network in D2D-based SNS, we propose an autonomous device placement algorithm. In this method, some relay devices are placed so as to increase the number of links in the physical network. Here, relay devices can be used only for relaying data and those are not SNS users. For example, unmanned aerial vehicles (UAV) with D2D communication capability and base stations with D2D communication capability are used as the relay devices. In the proposed method, at first, an optimization problem for minimizing node resilience which is a performance metric in order to place relay devices. Then, we investigate how relay devices should be placed based on some approximate optimal solutions. From this investigation, we propose an autonomous relay device placement in the physical network. In our proposed algorithm, relay devices can be placed without the complete information on network topology. We evaluate the performance of the proposed method with simulation, and investigate the effectiveness of the proposed method. From numerical examples, we show the effectiveness of our proposed algorithm.

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Averting Cascading Failures in Networked Infrastructures: Poset-Constrained Graph Algorithms

Cited by 20 publications

References 36 publications

MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

MEGA: Machine Learning-Enhanced Graph Analytics for Infodemic Risk Management

Epidemic Source Detection in Contact Tracing Networks: Epidemic Centrality in Graphs and Message-Passing Algorithms

Autonomous Relay Device Placement Algorithm for Avoiding Cascading Failure in D2D-Based Social Networking Service

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