Dynamical Clustering in Electronic Commerce Systems via Optimization and Leadership Expansion

Li, Hui-Jia; Bu, Zhan; Wang, Zhen

doi:10.1109/tii.2019.2960835

Cited by 135 publications

(56 citation statements)

References 27 publications

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“…Using the epidemiological characteristics of these countries to predict development trends in Africa may be biased. There are some new methods, such as multi-scale asynchronous belief percolation model, temporal networks, and dynamical clustering in electronic commerce systems [31][32][33], that may improve the limitations in this article. We will use these methods in the future research.…”

Section: Discussionmentioning

confidence: 99%

Epidemic Characteristics of COVID-19 in Africa

et al. 2020

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The aim of this study was to investigate coronavirus disease 2019 (COVID-19) epidemic characteristics in Africa and provide some references to prevent pandemic. Methods: We collect information of the laboratory-confirmed case of COVID-19 that has been reported since April 28, 2020 in Africa, describe the epidemic characteristics in different regions of Africa, and predict the spread trend. Results: (1) There are 35,309 COVID-19 cases and 1,522 deaths in 54 African countries. Both case amount and death toll in Northern Africa are higher than those in other African regions, whereas the case fatality rate (CFR) in Africa is lower than that of the world. (2) Among the 54 countries in Africa, 39 of them (72.22%) report confirmed cases between 1 and 499. (3) In total, 596,146 tests have been conducted in 35 countries, and 28,271 patients have been confirmed, with a positive rate of 4.74%. However, there are only 335 tests (interquartile range: 145.5-874) for every 1 million people in Africa. (4) The number of infection cases will increase to around 150,000 at the end of May according to our model. Conclusion: In Africa, COVID-19 spreads faster while with a relatively lower CFR, and it may be related to higher young people ratio. In addition, test intensity of COVID-19 is obviously insufficient in Africa.

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Section: Discussionmentioning

confidence: 99%

Epidemic Characteristics of COVID-19 in Africa

et al. 2020

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“…Weight optimization: The weight values can be optimized to improve the performance as demonstrated in [41], [42]. In this paper, this is achieved through hyper-parameter optimization using 25% of the dataset (as mentioned in Section III).…”

Section: A Ensemble Solution Setupmentioning

confidence: 99%

Do Many Models Make Light Work? Evaluating Ensemble Solutions for Improved Rumor Detection

Kim

et al. 2020

IEEE Access

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There have been many efforts to detect rumors using various machine learning (ML) models, but there is still a lack of understanding of their performance against different rumor topics and available features, resulting in a significant performance degrade against completely new and unseen (unknown) rumors. To address this issue, we investigate the relationship between ML models, features, and rumor topics to select the best rumor detection model under specific conditions using 13 different ML models. Our experiment results demonstrate that there is no clear winner among the ML models in all different rumor topics with respect to the detection performance. To overcome this problem, a possible way is to use an ensemble of ML models. Although previous work presents an improved detection of rumors using ensemble solutions (ES), their evaluation did not consider detecting unknown rumors. Further, they did not present nor evaluate the configuration of the ES to ensure that it indeed performs better than using a single ML model. Based on these observations, we propose to evaluate the use of an ES by examining their unknown rumor detection performance compared with single ML models but as well as different configurations of the ESes. Our experimental results using real-world datasets found that an ES of Random Forest, XGBoost and Multilayer perceptron overall produced the best F1 score of 0.79 for detecting unknown rumors, a significant improvement compared with a single best ML model which only achieved a 0.58 F1 score. We also showed that not all ESes are the same, with significantly degraded detection and large variations in performance when different ML models are used to construct the ES. Hence, it is infeasible to rely on any single ML model-based rumor detector. Finally, our solution also performed better than other recent detectors, such as eventAI and NileTMRG that performed similar to using a single ML model-making it a much more attractive solution to detect unknown rumors in practice.

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“…The second step is the information iterative process. During the interaction of the nodes in the dynamic system [7], they will comprehensively consider the decision based on the information of the surrounding nodes. The last step is to uncover the community structure.…”

Section: Basic Ideamentioning

confidence: 99%

Community Detection Based on a Preferential Decision Model

et al. 2020

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The research on complex networks is a hot topic in many fields, among which community detection is a complex and meaningful process, which plays an important role in researching the characteristics of complex networks. Community structure is a common feature in the network. Given a graph, the process of uncovering its community structure is called community detection. Many community detection algorithms from different perspectives have been proposed. Achieving stable and accurate community division is still a non-trivial task due to the difficulty of setting specific parameters, high randomness and lack of ground-truth information. In this paper, we explore a new decision-making method through real-life communication and propose a preferential decision model based on dynamic relationships applied to dynamic systems. We apply this model to the label propagation algorithm and present a Community Detection based on Preferential Decision Model, called CDPD. This model intuitively aims to reveal the topological structure and the hierarchical structure between networks. By analyzing the structural characteristics of complex networks and mining the tightness between nodes, the priority of neighbor nodes is chosen to perform the required preferential decision, and finally the information in the system reaches a stable state. In the experiments, through the comparison of eight comparison algorithms, we verified the performance of CDPD in real-world networks and synthetic networks. The results show that CDPD not only has better performance than most recent algorithms on most datasets, but it is also more suitable for many community networks with ambiguous structure, especially sparse networks.

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Dynamical Clustering in Electronic Commerce Systems via Optimization and Leadership Expansion

Cited by 135 publications

References 27 publications

Epidemic Characteristics of COVID-19 in Africa

Epidemic Characteristics of COVID-19 in Africa

Do Many Models Make Light Work? Evaluating Ensemble Solutions for Improved Rumor Detection

Community Detection Based on a Preferential Decision Model

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