Browsing by Author "Chin, Wei-Chien-Benny"

Now showing 1 - 2 of 2

Beyond bond links in complex networks:Local bridges, global bridges and silk links
(2019-12-15)
Huang, Chung-Yuan
;
Chin, Wei-Chien-Benny
;
Fu, Yu-Hsiang
;
Tsai, Yu-Shiuan
;
資訊工程學系
Many network researchers use intuitive or basic definitions when discussing the importance of strong and weak links and their roles. Others use an approach best described as "if not strong, then weak" to determine the strengths and weaknesses of individual links, thus deemphasizing hierarchical network structures that allow links to express different strength levels. Here we describe our proposal for a hierarchical edge type analysis (HETA) algorithm for determining link types at multiple network hierarchy levels based on the common neighbor concept plus statistical factors such as bond links, kth-layer local bridges, global bridges, and silk links-all generated during long-term network development and evolution processes. Two sets of networks were used to validate our proposed algorithm, one consisting of 16 networks employed in multiple past studies, and one consisting of two types of one-dimensional small-world networks expressing different random rewiring or shortcut addition probabilities. Two applications with potential for developmental contributions are demonstrated: a network fingerprint analysis framework, and a hierarchical network community partition method. (C) 2019 The Authors. Published by Elsevier B.V.
EpiRank: Modeling Bidirectional Disease Spread in Asymmetric Commuting Networks
(2019-04-01)
Huang, Chung-Yuan
;
Chin, Wei-Chien-Benny
;
Wen, Tzai-Hung
;
Fu, Yu-Hsiang
;
Tsai, Yu-Shivan
Commuting network flows are generally asymmetrical, with commuting behaviors bi-directionally balanced between home and work locations, and with weekday commutes providing many opportunities for the spread of infectious diseases via direct and indirect physical contact. The authors use a Markov chain model and PageRank-like algorithm to construct a novel algorithm called EpiRank to measure infection risk in a spatially confined commuting network on Taiwan island. Data from the country's 2000 census were used to map epidemic risk distribution as a commuting network function. A daytime parameter was used to integrate forward and backward movement in order to analyze daily commuting patterns. EpiRank algorithm results were tested by comparing calculations with actual disease distributions for the 2009 H1N1 influenza outbreak and enterovirus cases between 2000 and 2008. Results suggest that the bidirectional movement model outperformed models that considered forward or backward direction only in terms of capturing spatial epidemic risk distribution. EpiRank also outperformed models based on network indexes such as PageRank and HITS. According to a sensitivity analysis of the daytime parameter, the backward movement effect is more important than the forward movement effect for understanding a commuting network's disease diffusion structure. Our evidence supports the use of EpiRank as an alternative network measure for analyzing disease diffusion in a commuting network.