Viterbi Algorithm and HMM Implementation to Multicriteria Data-Driven Decision Support Model for Optimization of Medical Service Quality Selection

Mizera–Pietraszko, Jolanta; Tancula, Jolanta

doi:10.1007/978-3-030-99587-4_30

Cited by 1 publication

(1 citation statement)

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“…Similarly, also inference in hidden Markov models finds application in the medical domain. For example, inference in hidden Markov models is used for monitoring patients in intensive care units [91] or for guiding the selection of medical services [95]. However, the state-of-the-art trustworthy approaches to inference in hidden Markov models are still classic algorithms with limited scalability, developed prior to the big data era.…”

Section: Introductionmentioning

confidence: 99%

Span-core Decomposition for Temporal Networks

Galimberti

Ciaperoni

Barrat

et al. 2020

ACM Trans. Knowl. Discov. Data

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When analyzing temporal networks, a fundamental task is the identification of dense structures (i.e., groups of vertices that exhibit a large number of links), together with their temporal span (i.e., the period of time for which the high density holds). In this article, we tackle this task by introducing a notion of temporal core decomposition where each core is associated with two quantities, its coreness, which quantifies how densely it is connected, and its span, which is a temporal interval: we call such cores span-cores . For a temporal network defined on a discrete temporal domain T , the total number of time intervals included in T is quadratic in | T |, so that the total number of span-cores is potentially quadratic in | T | as well. Our first main contribution is an algorithm that, by exploiting containment properties among span-cores, computes all the span-cores efficiently. Then, we focus on the problem of finding only the maximal span-cores , i.e., span-cores that are not dominated by any other span-core by both their coreness property and their span. We devise a very efficient algorithm that exploits theoretical findings on the maximality condition to directly extract the maximal ones without computing all span-cores. Finally, as a third contribution, we introduce the problem of temporal community search , where a set of query vertices is given as input, and the goal is to find a set of densely-connected subgraphs containing the query vertices and covering the whole underlying temporal domain T . We derive a connection between this problem and the problem of finding (maximal) span-cores. Based on this connection, we show how temporal community search can be solved in polynomial-time via dynamic programming, and how the maximal span-cores can be profitably exploited to significantly speed-up the basic algorithm. We provide an extensive experimentation on several real-world temporal networks of widely different origins and characteristics. Our results confirm the efficiency and scalability of the proposed methods. Moreover, we showcase the practical relevance of our techniques in a number of applications on temporal networks, describing face-to-face contacts between individuals in schools. Our experiments highlight the relevance of the notion of (maximal) span-core in analyzing social dynamics, detecting/correcting anomalies in the data, and graph-embedding-based network classification.

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Section: Introductionmentioning

confidence: 99%