Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation…
Abstract
Purpose
Community detection is a significant research field in the study of social networks and analysis because of its tremendous applicability in multiple domains such as recommendation systems, link prediction and information diffusion. The majority of the present community detection methods considers either node information only or edge information only, but not both, which can result in loss of important information regarding network structures. In real-world social networks such as Facebook and Twitter, there are many heterogeneous aspects of the entities that connect them together such as different type of interactions occurring, which are difficult to study with the help of homogeneous network structures. The purpose of this study is to explore multilayer network design to capture these heterogeneous aspects by combining different modalities of interactions in single network.
Design/methodology/approach
In this work, multilayer network model is designed while taking into account node information as well as edge information. Existing community detection algorithms are applied on the designed multilayer network to find the densely connected nodes. Community scoring functions and partition comparison are used to further analyze the community structures. In addition to this, analytic hierarchical processing-technique for order preference by similarity to ideal solution (AHP-TOPSIS)-based framework is proposed for selection of an optimal community detection algorithm.
Findings
In the absence of reliable ground-truth communities, it becomes hard to perform evaluation of generated network communities. To overcome this problem, in this paper, various community scoring functions are computed and studied for different community detection methods.
Research limitations/implications
In this study, evaluation criteria are considered to be independent. The authors observed that the criteria used are having some interdependencies, which could not be captured by the AHP method. Therefore, in future, analytic network process may be explored to capture these interdependencies among the decision attributes.
Practical implications
Proposed ranking can be used to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the case study. The suggested study ranks existing community detection algorithms to find the most appropriate one.
Social implications
Community detection is useful in many applications such as recommendation systems, health care, politics, economics, e-commerce, social media and communication network.
Originality/value
Ranking of the community detection algorithms is performed using community scoring functions as well as AHP-TOPSIS methods.
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Sung-Woo Lee, Sung-Ho Shin and Hee-Sung Bae
This study aims to analyze information on vessel traffic between the two Koreas with a probability distribution for each route/vessel type. The study will then conduct an estimate…
Abstract
This study aims to analyze information on vessel traffic between the two Koreas with a probability distribution for each route/vessel type. The study will then conduct an estimate for maritime transport patterns of inter-Korean trade in the future. To analyze the flow of inter-Korean coastal shipping, this study conducted visualization analysis of shipping status between North and South Korea by year, ship type, and port using navigation data of three years from Port Logistics Information System (Port-MIS) sources during 2006 to 2008, which saw the most active exchanges between the two governments. Also, this study analyzes shipping status between the two governments as a probability distribution for each port and provides the prospects for future maritime transport for inter-Korean trade by means of Bayesian Networks and simulation. The results of the analysis are as follows: i) when North-South routes are reopened, the import volume for sand from North Korea will be increased; ii) investment in the modernization of ports in North Korea is required so that shipping companies can generate profit through economies of scale; iii) the number of the operating vessels including container ships between the two governments is expected to increase like when the tensions and conflict on the Korean Peninsula was release, especially between Busan port in South Korea and Nampo port in North Korea; and iv) among container ships, transshipment containers imported and exported through Busan Port will be shipped to North Korea by feeder transportation.
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Chunlei Li, Chaodie Liu, Zhoufeng Liu, Ruimin Yang and Yun Huang
The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile…
Abstract
Purpose
The purpose of this paper is to focus on the design of automated fabric defect detection based on cascaded low-rank decomposition and to maintain high quality control in textile manufacturing.
Design/methodology/approach
This paper proposed a fabric defect detection algorithm based on cascaded low-rank decomposition. First, the constructed Gabor feature matrix is divided into a low-rank matrix and sparse matrix using low-rank decomposition technique, and the sparse matrix is used as priori matrix where higher values indicate a higher probability of abnormality. Second, we conducted the second low-rank decomposition for the constructed texton feature matrix under the guidance of the priori matrix. Finally, an improved adaptive threshold segmentation algorithm was adopted to segment the saliency map generated by the final sparse matrix to locate the defect regions.
Findings
The proposed method was evaluated on the public fabric image databases. By comparing with the ground-truth, the average detection rate of 98.26% was obtained and is superior to the state-of-the-art.
Originality/value
The cascaded low-rank decomposition was first proposed and applied into the fabric defect detection. The quantitative value shows the effectiveness of the detection method. Hence, the proposed method can be used for accurate defect detection and automated analysis system.
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Qingchen Qiu, Xuelian Wu, Zhi Liu, Bo Tang, Yuefeng Zhao, Xinyi Wu, Hongliang Zhu and Yang Xin
This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI…
Abstract
Purpose
This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements.
Design/methodology/approach
First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency.
Findings
The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist.
Originality value
The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.
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Dan Yuan, Jiejie Du, Yaguang Pan and Chenxi Li
This study explores the role of industrial co-agglomeration and digital economy in influencing the green high-quality development of the Yellow River National Cultural Park to…
Abstract
Purpose
This study explores the role of industrial co-agglomeration and digital economy in influencing the green high-quality development of the Yellow River National Cultural Park to provide countermeasures and suggestions for promoting the whole-area high-quality development.
Design/methodology/approach
This study is based on panel data from 56 cities from 2010 to 2022. First, a Super-SBM model is built to evaluate green high-quality development. Secondly, location entropy is used to measure industrial co-agglomeration and the entropy weight method is used to measure the digital economy. Finally, the panel Tobit model is used to analyze the impact of industrial co-agglomeration and digital economy on the green high-quality development of Yellow River National Cultural Park.
Findings
This study found that (1) industrial co-agglomeration has a negative implication in green high-quality development, while the digital economy boosts green high-quality development; (2) industrial co-agglomeration is a less critical dependency on the level of development of the digital economy in influencing green high-quality development, while the facilitating effect of the digital economy is more dependent on industrial co-agglomeration and (3) the trend of slow growth in industrial co-agglomeration and digital economy development, with significant regional differences in green high-quality development.
Research limitations/implications
Undeniably, our study has several limitations. Firstly, as the study area only includes some cities in individual provinces, such as Qinghai, this paper only analyzes at the city level, which does not better reflect the differences between provinces; secondly, this study only adopts one method to determine the digital economy. In the future, other methods can be explored to measure digital economy; finally, in addition to the main role of digital economy and industrial co-agglomeration, other factors may also affect the green high-quality development of YRNCP. Future research should introduce other variables to improve the theoretical framework.
Practical implications
First, it provides countermeasures and suggestions for promoting the green high-quality development of YRNCP. Second, it helps to implement the new development concept, cultivate the new quality productivity of culture and the tourism industry and promote the green high-quality development of YRNCP. Third, it provides references to improve the management measures and related policies of the YRNCP more accurately and efficiently. Fourth, it helps to build a new development pattern and has important practical significance in promoting the high-quality development of the whole basin, protecting and inheriting the Yellow River Culture and helping the Chinese-style modernization and development, which are of great practical significance.
Social implications
The research is carried out from the new perspective of industrial co-agglomeration and digital economy, which provides the theoretical basis and reference for solving the problem of green high-quality development of YRNCP. Second, it broadens the research idea of green high-quality development. Third, it quantitatively analyzes the impact of industrial co-agglomeration and digital economy on the high-quality development of YRNCP, deepening the research on the green high-quality development of YRNCP. Fourth, it helps to enrich and improve the theoretical research related to the national cultural park development and has positive significance in promoting the management and innovation of the cultural industry and the construction of related disciplines.
Originality/value
The paper’s findings illustrate the functional relationship of the digital economy and industrial co-agglomeration with green high-quality development and propose countermeasures to facilitate the high-quality development of the Yellow River National Cultural Park.
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This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on…
Abstract
This paper considers locating congested fast charging stations (FCSs) and deploying chargers in a stochastic environment, while the related studies have predominantly focused on problems in deterministic environments. Reducing the inconvenience caused by congestion at FCSs is an important challenge for FCS service provider. This is the underlying motivation for this study to consider a problem for FCS network design with the congestion restriction in a stochastic environment. We proposed a maximal coverage problem subject to budget constraints and a congestion restriction in order to maximize the demand coverage. With the derivation of the congestion restriction in the considered stochastic environment, the problem is formulated into an integer programming model. A real-life case study is conducted and managerial implications are drawn from its results.
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Jiaqi Fang, Kun Ma, Yanfang Qiu, Ke Ji, Zhenxiang Chen and Bo Yang
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant…
Abstract
Purpose
The discrepancy between the content of an article and its title is a key characteristic of fake news. Current methods for detecting fake news often ignore the significant difference in length between the content and its title. In addition, relying solely on textual discrepancies between the title and content to distinguish between real and fake news has proven ineffective. The purpose of this paper is to develop a new approach called semantic enhancement network with content–title discrepancy (SEN–CTD), which enhances the accuracy of fake news detection.
Design/methodology/approach
The SEN–CTD framework is composed of two primary modules: the SEN and the content–title comparison network (CTCN). The SEN is designed to enrich the representation of news titles by integrating external information and position information to capture the context. Meanwhile, the CTCN focuses on assessing the consistency between the content of news articles and their corresponding titles examining both emotional tones and semantic attributes.
Findings
The SEN–CTD model performs well on the GossipCop, PolitiFact and RealNews data sets, achieving accuracies of 80.28%, 86.88% and 84.96%, respectively. These results highlight its effectiveness in accurately detecting fake news across different types of content.
Originality/value
The SEN is specifically designed to improve the representation of extremely short texts, enhancing the depth and accuracy of analyses for brief content. The CTCN is tailored to examine the consistency between news titles and their corresponding content, ensuring a thorough comparative evaluation of both emotional and semantic discrepancies.
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The purpose of this study is to establish a hierarchy of critical success factors to develop a framework for evaluating the performance of smart grids from a sustainability…
Abstract
Purpose
The purpose of this study is to establish a hierarchy of critical success factors to develop a framework for evaluating the performance of smart grids from a sustainability perspective.
Design/methodology/approach
The fuzzy analytical hierarchy process is used in this study to assess and determine the relative weight of economic, operational and environmental criteria. At the same time, the evidential reasoning algorithm is used to determine the belief degree of expert’s opinion, and the expected utility theory for the crisp value of success factors in performance estimation.
Findings
The finding reveals that success factors associated with the economic criteria receive significantly more attention from the expert group. Sensitivity analysis indicates the ranking of consumer satisfaction remains stable no matter how criteria weights are changed, which verifies the robustness and effectiveness of the proposed model and evaluation results.
Originality/value
The study presents a solid mathematical framework for collaborative system modeling and systematic analysis. Managers and stakeholders may use the proposed technique as a flexible tool to improve the energy system’s resiliency in a systematic way.
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The results showed that the use of a magnetic marker could relatively accurately reflect the fracture pattern inside the rock-like material (RLM).
Abstract
Purpose
The results showed that the use of a magnetic marker could relatively accurately reflect the fracture pattern inside the rock-like material (RLM).
Design/methodology/approach
This study investigated the internal structure and fracture pattern of a fractured RLM. Magnetized iron oxide powder, which was used as a magnetic marker, was mixed with water and glue to form a magnetic slurry, which was subsequently injected into a fractured RLM. After the magnetic slurry completely filled the cracks inside the RLM and became cemented, the distribution and magnitude of the magnetic field inside the RLM were determined using a three-dimensional (3D) magnetic field imaging system.
Findings
A model for determining the magnetic field strength was developed using MATLAB.
Originality/value
This model of 3D magnetic will further be used as a finite element tool to simulate and image cracks inside the rock.
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Yong Sun, Ya-Feng Zhang, Yalin Wang and Sihui Zhang
This paper aims to investigate the cooperative governance mechanisms for personal information security, which can help enrich digital governance research and provide a reference…
Abstract
Purpose
This paper aims to investigate the cooperative governance mechanisms for personal information security, which can help enrich digital governance research and provide a reference for the formulation of protection policies for personal information security.
Design/methodology/approach
This paper constructs an evolutionary game model consisting of regulators, digital enterprises and consumers, which is combined with the simulation method to examine the influence of different factors on personal information protection and governance.
Findings
The results reveal seven stable equilibrium strategies for personal information security within the cooperative governance game system. The non-compliant processing of personal information by digital enterprises can damage the rights and interests of consumers. However, the combination of regulatory measures implemented by supervisory authorities and the rights protection measures enacted by consumers can effectively promote the self-regulation of digital enterprises. The reputation mechanism exerts a restricting effect on the opportunistic behaviour of the participants.
Research limitations/implications
The authors focus on the regulation of digital enterprises and do not consider the involvement of malicious actors such as hackers, and the authors will continue to focus on the game when assessing the governance of malicious actors in subsequent research.
Practical implications
This study's results enhance digital governance research and offer a reference for developing policies that protect personal information security.
Originality/value
This paper builds an analytical framework for cooperative governance for personal information security, which helps to understand the decision-making behaviour and motivation of different subjects and to better address issues in the governance for personal information security.