A time evolving online social network generation algorithm

Shirzadian, Pouyan; Antony, Blessy; Gattani, Akshaykumar G.; Tasnina, Nure; Heath, Lenwood S.

doi:10.1038/s41598-023-29443-w

Cited by 4 publications

(2 citation statements)

References 53 publications

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“…In the context of patient health care data, a high-fidelity synthetic dataset would be able to capture complex clinical relationships and be clinically indistinguishable from real patient data [11][12][13]. Within the realm of social network analysis, a high-fidelity synthetic dataset would accurately capture the intricate connections, community structures, and communication patterns present in real social networks [14]. It would possess the same statistical properties, network topologies, and user behaviours, rendering it virtually indistinguishable from genuine social network data.…”

Section: High-fidelity Synthetic Datamentioning

confidence: 99%

High-Fidelity Synthetic Data Applications for Data Augmentation

Wang,

Draghi,

Rotalinti

et al. 2024

Artificial Intelligence

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The use of high-fidelity synthetic data for data augmentation is an area of growing interest in data science. In this chapter, the concept of synthetic data is introduced, and different types of synthetic data are discussed in terms of their utility or fidelity. Approaches to synthetic data generation are presented and compared with computer modelling and simulation approaches, highlighting the unique benefits of high-fidelity synthetic data. One of the main applications of high-fidelity synthetic data is supporting the training and validation of machine learning algorithms, where it can provide a virtually unlimited amount of diverse and high-quality data to improve the accuracy and robustness of models. Furthermore, high-fidelity synthetic data can address missing data and biases due to under-sampling using techniques such as BayesBoost, as well as boost sample sizes in scenarios where the real data is based on a small sample. Another important application is generating virtual patient cohorts, such as digital twins, to estimate counterfactuals in silico trials, allowing for better prediction of treatment outcomes and personalised medicine. The chapter concludes by identifying areas for further research in the field, including developing more efficient and accurate synthetic data generation methods and exploring the ethical implications of using synthetic data.

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Section: High-fidelity Synthetic Datamentioning

confidence: 99%

High-Fidelity Synthetic Data Applications for Data Augmentation

Wang,

Draghi,

Rotalinti

et al. 2024

Artificial Intelligence

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show abstract

“…In modern scientific works, researchers are developing various methods for analyzing clusters in social networks [54]. The structure of social networks is analyzed from three levels: micro-level, meso-level, and macro-level based on the regular graph model, exponential random graph model, small world network model, and network model without scale [55], various models and algorithms for generating social networks are also being developed [56]. Review articles [57][58][59] provide a detailed analysis of current advances in identifying important nodes from a social network perspective, as well as the various social network centrality measures that have been developed.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

Shevcov,

Ustin,

Popov

et al. 2023

OBM Neurobiol

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The sudden spread of the COVID-19 pandemic has convincingly proved the role of social networks in human life activity as an actor of interpersonal relations. The need for isolation and the limitation of face-to-face communication between people has significantly transformed the system of interpersonal connections through an extensive increase in social contacts in the virtual environment and the growing importance of online social services. In the study, we addressed the problem of diagnosing the indicator of social success of a personality, reflecting characteristics of its interpersonal relations in offline activity through the attributes of its virtual activity in social networks. The research was based on the methods of social network analysis and traditional ways of psychodiagnostics. Social networks were analyzed using social graphs - mathematical models that describe the characteristics of relationships between users in social networks through various metrics (friends, elements, edges, density, closeness centrality, degree centrality, clustering coefficient, etc.). The study aims to prove the predictive validity of social graph indicators as predictors of personality social success through correlations of graph characteristics reflecting the features of interpersonal relations of a social network user in a virtual environment with the socio-psychological hands of traditional psychodiagnostic tools. The study included 601 subjects. Data was collected using psychological questionnaires and personal profiles from the social network VKontakte. The results of the study showed correlations between several characteristics of social graphs (density, clustering coefficient, closeness centrality, etc.) and several indicators of psychodiagnostic techniques (Dominance index in relationships with others, Organizational abilities, Desire for people, Narcissism, etc.). The significant contribution of the research is to expand the methodological apparatus of psychological science and to open new methods of predicting interpersonal relations of personality through its activity in a virtual environment.

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An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

Karami,

Niaki

2024

Neural Networks

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A time evolving online social network generation algorithm

Cited by 4 publications

References 53 publications

High-Fidelity Synthetic Data Applications for Data Augmentation

High-Fidelity Synthetic Data Applications for Data Augmentation

Assessment of a Person's Social Success Through the Characteristics of Interpersonal Relationships in A Virtual Environment

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

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