Spectral Methods for Immunization of Large Networks

Ahmad, Muhammad Aurangzeb; Tariq, Juvaria; Shabbir, Mudassir; Khan, Imdadullah

doi:10.3127/ajis.v21i0.1563

Cited by 20 publications

(18 citation statements)

References 22 publications

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“…Using this idea and a kernel method, Ali et al in [20] accomplish higher accuracy than in [10], in the classification of different variants of SARS-CoV-2 in humans. Successful analysis of different variants leads to designing efficient strategy regarding the vaccination distribution [41][42][43][44].…”

Section: Literature Reviewmentioning

confidence: 99%

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

2021

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The widespread availability of large amounts of genomic data on the SARS-CoV-2 virus, as a result of the COVID-19 pandemic, has created an opportunity for researchers to analyze the disease at a level of detail, unlike any virus before it. On the one hand, this will help biologists, policymakers, and other authorities to make timely and appropriate decisions to control the spread of the coronavirus. On the other hand, such studies will help to more effectively deal with any possible future pandemic. Since the SARS-CoV-2 virus contains different variants, each of them having different mutations, performing any analysis on such data becomes a difficult task, given the size of the data. It is well known that much of the variation in the SARS-CoV-2 genome happens disproportionately in the spike region of the genome sequence—the relatively short region which codes for the spike protein(s). In this paper, we propose a robust feature-vector representation of biological sequences that, when combined with the appropriate feature selection method, allows different downstream clustering approaches to perform well on a variety of different measures. We use such proposed approach with an array of clustering techniques to cluster spike protein sequences in order to study the behavior of different known variants that are increasing at a very high rate throughout the world. We use a k-mers based approach first to generate a fixed-length feature vector representation of the spike sequences. We then show that we can efficiently and effectively cluster the spike sequences based on the different variants with the appropriate feature selection. Using a publicly available set of SARS-CoV-2 spike sequences, we perform clustering of these sequences using both hard and soft clustering methods and show that, with our feature selection methods, we can achieve higher F1 scores for the clusters and also better clustering quality metrics compared to baselines.

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Section: Literature Reviewmentioning

confidence: 99%

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

2021

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“…A combinatorial trace method is adopted in [3,30,2] to select a subset of nodes whose removal will result in the maximum reduction in the λ max . The trace of a large power of an adjacency matrix, A p , is closely related to the λ max (A) (also known as the spectral radius of the graph) [1].…”

Section: Related Workmentioning

confidence: 99%

“…• We extend the score function based on the number of closed walks of length 8 for sets of nodes that quantify the importance of sets to reduce the graph vulnerability defined in [3]. This score function is monotonically nondecreasing and sub-modular that enables employing greedily constructing a set with improved approximation quality • We derive a closed-form formula to compute the number of walks of length 8 passing through a node which may be of independent interest.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial trace method for network immunization

Ahmad

Ali

Tariq

et al. 2020

Information Sciences

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Immunizing a subset of nodes in a network -enabling them to identify and withstand the spread of harmful content -is one of the most effective ways to counter the spread of malicious content. It has applications in network security, public health policy, and social media surveillance. Finding a subset of nodes whose immunization results in the least vulnerability of the network is a computationally challenging task. In this work, we establish a relationship between a widely used network vulnerability measure and the combinatorial properties of networks. Using this relationship and graph summarization techniques, we propose an efficient approximation algorithm to find a set of nodes to immunize. We provide theoretical justifications for the proposed solution and analytical bounds on the runtime of our algorithm. We empirically demonstrate on various real-world networks that the performance of our algorithm is an order of magnitude better than the state of the art solution. We also show that in practice the runtime of our algorithm is significantly lower than that of the best-known solution.

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“…Even finding the single likeliest path of viral transmission through the network between a single pair of nodes is a variation of the Traveling Salesman problem, which is NP-hard. 5,6 As Arbore and Fioriti 7 discuss, under a simple susceptible/infected (SI) model of infection, a network has 2 N possible states, which would demand severe computational costs to even simulate. They and others suggest instead modeling the system as a set of N Markov processes, 7,8 but this only works if the network topology of transmission probabilities around each node is static over time.…”

Section: Introductionmentioning

confidence: 99%

“…Yet classical attempts to predict viral diffusion, whether through biological, technological, or social systems, are stifled by computational limits that render them intractable. Even finding the single likeliest path of viral transmission through the network between a single pair of nodes is a variation of the Traveling Salesman problem, which is NP‐hard …”

Section: Introductionmentioning

confidence: 99%

Modeling viral diffusion using quantum computational network simulation

Britt

2020

Quantum Engineering

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Summary Processes of viral diffusion are important in biological, technological, and social systems alike. Several mathematical models of infection have been developed to predict diffusion through networks, such as nonlinear dynamical systems (NLDSs). Such models generally offer accurate representations of real‐world diffusion, particularly for networks with static topologies. However, simulations of viral diffusion are computationally expensive, rendering them infeasible for large‐scale networks. Here, a new approach is shown that leverages quantum computing to make viral diffusion simulations feasible for large networks, independent of network topology. Simulations of an error‐free quantum circuit accurately modeled viral diffusion, with multivariate Euclidean distances from predicted infection probabilities capped near 8% for a network with N = 5 nodes and t = 20 time‐steps. This is sufficient accuracy to distinguish the relative susceptibility of nodes and to identify significant changes, such as periods of especially high susceptibility. The results illustrate the potential for quantum computational network simulation to provide accurate models of diffusion through large networks, an important real‐world application of quantum computing. The ability to simulate viral diffusion is invaluable for researchers across disciplines who aim to understand, anticipate, prepare for, and intervene in ongoing diffusion processes.

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Spectral Methods for Immunization of Large Networks

Cited by 20 publications

References 22 publications

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

Robust Representation and Efficient Feature Selection Allows for Effective Clustering of SARS-CoV-2 Variants

Combinatorial trace method for network immunization

Modeling viral diffusion using quantum computational network simulation

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