Browsing by Author "Fu, Yu-Hsiang"

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A community detection algorithm using network topologies and rule-based hierarchical arc-merging strategies
(2017-11-09)
Fu, Yu-Hsiang
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
The authors use four criteria to examine a novel community detection algorithm: (a) effectiveness in terms of producing high values of normalized mutual information (NMI) and modularity, using well-known social networks for testing; (b) examination, meaning the ability to examine mitigating resolution limit problems using NMI values and synthetic networks; (c) correctness, meaning the ability to identify useful community structure results in terms of NMI values and Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks; and (d) scalability, or the ability to produce comparable modularity values with fast execution times when working with large-scale real-world networks. In addition to describing a simple hierarchical arc-merging (HAM) algorithm that uses network topology information, we introduce rule-based arc-merging strategies for identifying community structures. Five well-studied social network datasets and eight sets of LFR benchmark networks were employed to validate the correctness of a ground-truth community, eight large-scale real-world complex networks were used to measure its efficiency, and two synthetic networks were used to determine its susceptibility to two resolution limit problems. Our experimental results indicate that the proposed HAM algorithm exhibited satisfactory performance efficiency, and that HAM-identified and ground-truth communities were comparable in terms of social and LFR benchmark networks, while mitigating resolution limit problems.
Beyond bond links in complex networks:Local bridges, global bridges and silk links
(2019-12-15)
Huang, Chung-Yuan
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Chin, Wei-Chien-Benny
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Fu, Yu-Hsiang
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Tsai, Yu-Shiuan
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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
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Chin, Wei-Chien-Benny
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Wen, Tzai-Hung
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Fu, Yu-Hsiang
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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.
Identify influential social network spreaders
(2014)
Huang, Chung-Yuan
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Fu, Yu-Hsiang
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, or contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of SusceptibleInfected- Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.
Identifying Super-Spreader Nodes in Complex Networks
(2015-01-01)
Fu, Yu-Hsiang
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
Identifying the most influential individuals spreading information or infectious diseases can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders, but efforts to use node diversity within network structures to measure spreading ability are few. Here we describe a two-step framework that combines global diversity and local features to identify the most influential network nodes. Results from susceptible-infected-recovered epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets.
Selecting Multiple Network Spreaders based on Community Structure using Two-Phase Evolutionary Framework
(2015)
Fu, Yu-Hsiang
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
The identification of multiple network spreaders is an appropriate solution to spread information, ideas or diseases in many practical applications. For instance, in target marketing, the spreaders are selected from customer groups classified by similar purchase behaviors to advertise the products, and to optimize the allocation of limited resources. The community detection approaches intuitively are used to identify the community structures or social groups in a social/complex network. However, how to determine the number of community K is a difficult issue. Hence, two-phase evolutionary framework (TPEF) is proposed for automatically determining the number of community K and maximizing the modularity of communities. In the preliminary experiment, the LFR benchmark networks are used to test the proposed method, and to analyze the execution time, the community quality and the network spreading effect. The experiment results show that TPEF can perform well and produce the satisfied quality of community structures. The community detection approaches can be used to assist selecting the multiple network spreaders, and to gain the benefit in network spreading when the community structure is obvious. Furthermore, our results suggest that developing an index, a mechanism or a sampling technic is necessary to decide whether the community detection approaches are applied for selecting multiple network spreaders.
Using a two-phase evolutionary framework to select multiple network spreaders based on community structure
(2016-11-01)
Fu, Yu-Hsiang
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
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資訊工程學系
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Department of Computer Science
Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders. (C) 2016 Elsevier B.V. All rights reserved.
Using global diversity and local features to identify influential social network spreaders
(2014-01-01)
Fu, Yu-Hsiang
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Huang, Chung-Yuan
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Sun, Chuen-Tsai
;
資訊工程學系
;
Department of Computer Science
The identification of influential spreaders of information via social networks can assist in the acceleration or hindrance of information dissemination, in increased product exposure, and in the detection of contagious disease outbreaks. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, researchers have overlooked node diversity within network structures as a means of measuring spreading ability. The two-step framework described in this paper uses a robust and insensitive measure that combines global diversity and local features (e.g., degree centrality) to identify the most influential social network nodes. Preliminary experiment results indicate that the proposed method performs well and maintains stability in single initial spreader scenarios associated with different social network datasets.
Using global diversity and local topology features to identify influential network spreaders
(2015-09-01)
Fu, Yu-Hsiang
;
Huang, Chung-Yuan
;
Sun, Chuen-Tsai
;
資訊工程學系
;
Department of Computer Science
Identifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible-Infected-Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets. (C) 2015 The Authors. Published by Elsevier B.V.