Chung-Yuan Huang

Innovative products that receive favorable reviews but never catch on with consumers belong to a category known as the “best game no one played.” We combined an adoption threshold model with an opinion dynamics model to examine reasons why certain high-quality products and ideas never achieve expected levels of commercial success. Computational social scientists use opinion dynamics models to analyze consensus formation, and adoption threshold models to study acceptance scenarios. However, most studies based on the first type focus on opinion exchanges without discussing follow-up actions, and most based on the second type only examine ways that individual decisions are dependent on numbers or proportions of friends and neighbors already engaged in specific behaviors, regardless of opinion differences. For this study, four kinds of theoretical networks (regular lattice, random, small-world, scale-free) served as underlying social network structures, and an agent-based simulation approach was used to analyze opinion exchange dynamics and product acceptance. Results indicate that computational agents were capable of changing pro/con opinions regarding issues, products, policies, etc. based on communication with neighboring agents via underlying social networks, and of making acceptance/rejection choices based on a combination of individual adoption threshold plus observations of their neighbors’ behaviors. A series of sensitivity analysis simulation experiments was conducted to identify model-related factors, determine non-linear correlations among them, and quantify degrees of influence. Factors exerting the strongest influence or requiring greater care when applied to cases of innovation diffusion were examined. Sensitivity analysis results indicate that agent adoption threshold mean exerted the greatest influence, followed by agent attitude mean and bounded confidence. Mechanism decomposition experiments revealed that the testimony effect neutralizes opinion clustering, making coordination failure the dominant driver of the opinion–adoption gap. These findings yield predictions distinguishing the model from information cascades, network externalities, and global games.

This protocol provides step-by-step instructions for reproducing the simulation results presented in the PLOS ONE paper "Exploring the Best Game No One Played Phenomenon Using A Mixed Opinion Dynamics and Innovation Diffusion Model." It covers environment setup, running the BCAT (Bounded Confidence and Adoption Threshold) model, executing sensitivity analysis, mechanism decomposition experiments, and finite-size scaling analysis across four network topologies (Regular Lattice, Small-World, Random, Scale-Free).

When controversial products are introduced, effective promotion efforts and eventual public acceptance require consideration of multiple factors such as existing social network structures, numbers of pioneers and their locations, and appropriate methods for product information diffusion. These factors have been the focus of marketing, investment and other studies for many years, most recently in computer information science. Researchers are especially motivated to understand diffusion processes for new technologies and controversial products within and across social networks. While many product diffusion simulation models have been proposed, most suffer from assumptions of unchanging internal agent attitudes toward products, no opinion exchanges between agents, and non-significant relationships between agent internal opinion attitudes and diffusion thresholds. In this paper we propose an opinion dynamics model that assumes both agent interaction and changes in agent attitudes over time. Social psychology theory is used to explain interactions between opinion and diffusion dynamics, with changing agent attitudes and behaviors affected by interpersonal relationship factors. Simulations were used to study dynamic diffusion processes involving controversial products (e.g., vaccines and genetically modified foods) in different social networks. Results indicate that the proposed model accurately reflects several kinds of social phenomena, including pioneer influences, rural marketing strategies, and the influence of social network structure. This effort to identify instances of product diffusion under various social conditions is offered in support of research in communication dynamics and social media-centered marketing strategies.

Huang et al. ¹ used their EpiRank algorithm, which emphasizes forward-and-backward commuter flow between homes and workplaces, to analyze the distribution patterns of two infectious diseases in Taiwan: the 2009-H1N1 influenza virus and the widespread emergence of the 2000-2008 type 71 enterovirus (EV). As this article was being prepared, the spreading mechanism of the novel coronavirus disease now designated as COVID-19 had yet to be identified, but according to the American Centers for Disease Control, its spreading mechanism and patterns are likely more similar to influenza than to other coronaviruses such as Severe Acute Respiratory Syndrome (SARS-CoV-1) or Middle East Respiratory Syndrome (MERS-CoV). To consider potential COVID-19 spatial patterns, we applied EpiRank to the 2003 SARS outbreak in north Taiwan for comparison with H1N1 and EV. SARS was found to be less contagious than H1N1 or EV, but with a significantly higher fatality rate. The characteristics of these diseases determined their specific spatial spreading patterns, as reflected in the different effects of forward and backward commuting movement. Our motivation is to highlight these differences and to illustrate EpiRank spatial patterns for the 2003 SARS outbreak for comparison with EpiRank-determined distributions for the H1N1 and EV outbreaks. Our results indicate that the daytime parameter (i.e., forward movement effect) range was slightly higher (0.5-0.55) for the SARS outbreak than for either the influenza (0.4-0.5) or EV (0.3-0.5) outbreaks, suggesting that the forward-and-backward movements of individuals between residential and core urban areas with concentrated populations were equally important regarding the spread of SARS. While COVID-19 might resemble either SARS or H1N1 in terms of spatial spreading, its daytime parameter is likely somewhere in-between, with backward movement being dominant (similar to H1N1) or with forward and backward movement being equally important (similar to SARS). Building on Huang et al.’s paper, we present an estimated risk distribution pattern for the Taipei Metropolitan Area for a daytime parameter of 0.55.

Many real-world networks consisting of nodes representing (in)tangible asymmetric information or energy flows must be modeled as directed graphs (digraphs). Several methods for classifying non-directional edges in terms of strong or weak ties have been developed for well-known non-directional networks, but none specifically for directed networks. In almost all cases, definitions and identification methods are simple, incomplete, reliant on intuition, and based on the assumption that anything that is not weak must be strong. Researchers have generally failed to consider overlapping and hierarchical community properties that accurately reflect organizational structures or the functional components commonly found in real-world complex networks, resulting in multiple challenges to analyzing many types of directed networks. In this paper we describe a method that considers asymmetric definitions of arc strength, especially when arcs hold important directional significance. To more fully capture overlapping and hierarchical network community structures, we used hierarchy-based definitions to identify bond arcs, kth-layer local bridges, global bridges, and silk arcs and to create a hierarchical arc type analysis (HATA) algorithm. The algorithm employs a mix of common middle node measures and statistical parameters generated by randomized directed networks corresponding to the network being investigated. To test the HATA algorithm, we conducted four experiments involving a mix of arc rewiring and additions, multiple datasets associated with the Travian game, 56 empirical networks from previous studies, and 3 bird song transition networks. Our results indicate that HATA offers a novel perspective to understanding arc strengths and structures in directed complex networks.

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.

Fast Conflict Detection for Two-Dimensional Packet Filters

The emerging disease of rabies was confirmed in Taiwan ferret badgers (FBs) and reported to the World Organization for Animal Health (OIE) on July 17, 2013. The spread of wildlife rabies can be related to neighborhood countries in Asia. The phylogenetic analysis was conducted by maximum likelihood (ML) methods and the Bayesian coalescent approach based on the glycoprotein (G) and nucleoprotein (N) genes. The phylogeographic and spatial temporal dynamics of viral transmission were determined by using SPREAD, QGIS. Therefore, the origin and the change with time of the viruses can be identified. Results showed the rabies virus of FB strains in Taiwan is a unique clade among other strains in Asia. According to the phylogeographic coalescent tree, three major genotypes of the FB rabies virus have circulated in three different geographical areas in Taiwan. Two genotypes have distributed into central and southern Taiwan between two ecological river barriers. The third genotype has been limited in southeastern Taiwan by the natural mountain barrier. The diversity of FB rabies viruses indicates that the biological profile of FBs could vary in different geographical areas in Taiwan. An enhanced surveillance system needs to be established near the currently identified natural barriers for early warnings of the rabies virus outbreak in Taiwan.