How to Optimize Social Network Influence

Zhan, F. Benjamin

doi:10.1109/aike.2019.00029

Cited by 9 publications

(1 citation statement)

References 46 publications

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“…In particular, attributes of the actors have been used to train clustering models, specifically through the K-means technique, thus corroborating that the SNA methodology and artificial intelligence coincide when it comes to generating cohesive communities or subgroups within a network. The use of artificial intelligence and social network analysis techniques is new in the field of health, and there are already studies that are combining both techniques to obtain results that are useful and that solve problems in the field of health [28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

et al. 2023

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Aim To study the existence of subgroups by exploring the similarities between the attributes of the nodes of the groups, in relation to diet and gender and, to analyse the connectivity between groups based on aspects of similarities between them through SNA and artificial intelligence techniques. Methods 235 students from 5 different educational centres participate in this study between March and December 2015. Data analysis carried out is divided into two blocks: social network analysis and unsupervised machine learning techniques. As for the social network analysis, the Girvan-Newman technique was applied to find the best number of cohesive groups within each of the friendship networks of the different classes analysed. Results After applying Girvan-Newman in the three classes, the best division into clusters was respectively 2 for classroom A, 7 for classroom B and 6 for classroom C. There are significant differences between the groups and the gender and diet variables. After applying K-means using population diet as an input variable, a K-means clustering of 2 clusters for class A, 3 clusters for class B and 3 clusters for class C is obtained. Conclusion Adolescents form subgroups within their classrooms. Subgroup cohesion is defined by the fact that nodes share similarities in aspects that influence obesity, they share attributes related to food quality and gender. The concept of homophily, related to SNA, justifies our results. Artificial intelligence techniques together with the application of the Girvan-Newman provide robustness to the structural analysis of similarities and cohesion between subgroups.

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Section: Introductionmentioning

confidence: 99%

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

et al. 2023

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An Evolutionary Approach to Compact DAG Neural Network Optimization

Chiu

Zhan

2019

IEEE Access

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Neural networks are the cutting edge of artificial intelligence, demonstrated to reliably outperform other techniques in machine learning. Within the domain of neural networks, many different classes of architectures have been developed for various tasks in specific subfields, as well as a multitude of diversity in the way of activation functions, loss functions, and other such hyperparameters. These networks are often large and computationally expensive to train and deploy, restricting their utility. Furthermore, the fundamental theory behind the effectiveness of particular network architectures and hyperparameters are often not well understood, and as such, practitioners frequently resort to trial-and-error techniques to optimize their model performance. To address these concerns, we propose the use of compact directed acyclic graph neural networks (DAG-NNs) and an evolutionary approach for automating the optimization of their structure and parameters. Our experimental results demonstrate that our approach consistently outperforms conventional neural networks, even while employing fewer nodes. INDEX TERMS Evolutionary algorithms, DAG neural networks, compact neural networks, artificial intelligence.

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Deep Physical Informed Neural Networks for Metamaterial Design

2020

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In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.

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How to Optimize Social Network Influence

Cited by 9 publications

References 46 publications

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

Adolescent relational behaviour and the obesity pandemic: A descriptive study applying social network analysis and machine learning techniques

An Evolutionary Approach to Compact DAG Neural Network Optimization

Deep Physical Informed Neural Networks for Metamaterial Design

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