FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3202265981> ?p ?o ?g. }

• W3202265981 abstract "Abstract The use of machine learning methods in classical and quantum systems has led to novel techniques to classify ordered and disordered phases, as well as uncover transition points in critical phenomena. Efforts to extend these methods to dynamical processes in complex networks is a field of active research. Network-percolation, a measure of resilience and robustness to structural failures, as well as a proxy for spreading processes, has numerous applications in social, technological, and infrastructural systems. A particular challenge is to identify the existence of a percolation cluster in a network in the face of noisy data. Here, we consider bond-percolation, and introduce a sampling approach that leverages the core-periphery structure of such networks at a microscopic scale, using onion decomposition, a refined version of the k -core. By selecting subsets of nodes in a particular layer of the onion spectrum that follow similar trajectories in the percolation process, percolating phases can be distinguished from non-percolating ones through an unsupervised clustering method. Accuracy in the initial step is essential for extracting samples with information-rich content, that are subsequently used to predict the critical transition point through the confusion scheme, a recently introduced learning method. The method circumvents the difficulty of missing data or noisy measurements, as it allows for sampling nodes from both the core and periphery, as well as intermediate layers. We validate the effectiveness of our sampling strategy on a spectrum of synthetic network topologies, as well as on two real-word case studies: the integration time of the US domestic airport network, and the identification of the epidemic cluster of COVID-19 outbreaks in three major US states. The method proposed here allows for identifying phase transitions in empirical time-varying networks." @default.
• W3202265981 created "2021-10-11" @default.
• W3202265981 creator A5052965447 @default.
• W3202265981 creator A5078917608 @default.
• W3202265981 date "2022-03-09" @default.
• W3202265981 modified "2023-09-26" @default.
• W3202265981 title "A sampling-guided unsupervised learning method to capture percolation in complex networks" @default.
• W3202265981 cites W1901616594 @default.
• W3202265981 cites W1987111416 @default.
• W3202265981 cites W1996639348 @default.
• W3202265981 cites W2007300619 @default.
• W3202265981 cites W2019334471 @default.
• W3202265981 cites W2030539428 @default.
• W3202265981 cites W2043530395 @default.
• W3202265981 cites W2044881936 @default.
• W3202265981 cites W2045313237 @default.
• W3202265981 cites W2072053277 @default.
• W3202265981 cites W2083719221 @default.
• W3202265981 cites W2086567524 @default.
• W3202265981 cites W2118978333 @default.
• W3202265981 cites W2141113219 @default.
• W3202265981 cites W2147441165 @default.
• W3202265981 cites W2169015768 @default.
• W3202265981 cites W2169404653 @default.
• W3202265981 cites W2169712321 @default.
• W3202265981 cites W2290198435 @default.
• W3202265981 cites W2337082154 @default.
• W3202265981 cites W2414456771 @default.
• W3202265981 cites W2419175238 @default.
• W3202265981 cites W2531147647 @default.
• W3202265981 cites W2594041373 @default.
• W3202265981 cites W2607839392 @default.
• W3202265981 cites W2748390680 @default.
• W3202265981 cites W2749578364 @default.
• W3202265981 cites W2765597272 @default.
• W3202265981 cites W2766323574 @default.
• W3202265981 cites W2791544114 @default.
• W3202265981 cites W2795407028 @default.
• W3202265981 cites W2796090547 @default.
• W3202265981 cites W2808878465 @default.
• W3202265981 cites W2921179351 @default.
• W3202265981 cites W2934958788 @default.
• W3202265981 cites W2962705874 @default.
• W3202265981 cites W2971450710 @default.
• W3202265981 cites W2979848992 @default.
• W3202265981 cites W2983684261 @default.
• W3202265981 cites W2988607083 @default.
• W3202265981 cites W3011997914 @default.
• W3202265981 cites W3025951569 @default.
• W3202265981 cites W3094683386 @default.
• W3202265981 cites W3102288906 @default.
• W3202265981 cites W3103657871 @default.
• W3202265981 cites W3103722330 @default.
• W3202265981 cites W3104154397 @default.
• W3202265981 cites W3104239185 @default.
• W3202265981 cites W3112934818 @default.
• W3202265981 cites W3148678985 @default.
• W3202265981 cites W3158814607 @default.
• W3202265981 cites W3160620840 @default.
• W3202265981 cites W3175954331 @default.
• W3202265981 cites W849918760 @default.
• W3202265981 doi "https://doi.org/10.1038/s41598-022-07921-x" @default.
• W3202265981 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35264699" @default.
• W3202265981 hasPublicationYear "2022" @default.
• W3202265981 type Work @default.
• W3202265981 sameAs 3202265981 @default.
• W3202265981 citedByCount "1" @default.
• W3202265981 countsByYear W32022659812023 @default.
• W3202265981 crossrefType "journal-article" @default.
• W3202265981 hasAuthorship W3202265981A5052965447 @default.
• W3202265981 hasAuthorship W3202265981A5078917608 @default.
• W3202265981 hasBestOaLocation W32022659811 @default.
• W3202265981 hasConcept C104317684 @default.
• W3202265981 hasConcept C106131492 @default.
• W3202265981 hasConcept C119857082 @default.
• W3202265981 hasConcept C121332964 @default.
• W3202265981 hasConcept C121864883 @default.
• W3202265981 hasConcept C124101348 @default.
• W3202265981 hasConcept C136764020 @default.
• W3202265981 hasConcept C140779682 @default.
• W3202265981 hasConcept C149288129 @default.
• W3202265981 hasConcept C154945302 @default.
• W3202265981 hasConcept C158574103 @default.
• W3202265981 hasConcept C164154869 @default.
• W3202265981 hasConcept C169760540 @default.
• W3202265981 hasConcept C177634923 @default.
• W3202265981 hasConcept C185592680 @default.
• W3202265981 hasConcept C2780457167 @default.
• W3202265981 hasConcept C31972630 @default.
• W3202265981 hasConcept C34947359 @default.
• W3202265981 hasConcept C41008148 @default.
• W3202265981 hasConcept C55493867 @default.
• W3202265981 hasConcept C62520636 @default.
• W3202265981 hasConcept C63479239 @default.
• W3202265981 hasConcept C73555534 @default.
• W3202265981 hasConcept C80444323 @default.
• W3202265981 hasConcept C86803240 @default.
• W3202265981 hasConceptScore W3202265981C104317684 @default.
• W3202265981 hasConceptScore W3202265981C106131492 @default.
• W3202265981 hasConceptScore W3202265981C119857082 @default.

• next