A review on community structures detection in time evolving social networks

Alotaibi, Norah; Rhouma, Delel

doi:10.1016/j.jksuci.2021.08.016

Cited by 18 publications

(8 citation statements)

References 39 publications

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“…Figure 4a suggests that most of the individuals are in fewer than 20 communities with more than half of them in at most 10 communities. This is in line with the report on Facebook groups and their impact published in 2021 49,55 , which states that 1.8 billion individuals are a part of one or more groups with more than half of them in 5 or more groups.…”

Section: Number Of Individuals and Connectionssupporting

confidence: 89%

“…A community in a network is a designated set of nodes that are typically highly connected. This community structure is one of the critical features that characterize real-world online social networks 48 , 49 . A network can have multiple communities; these can be overlapping or non-overlapping.…”

Section: Preliminariesmentioning

confidence: 99%

“…As observed in real-world social networks 51 , 52 , the evolution of the community structure in the network includes the occasional merger of two or more existing communities to form a new community. Usually, these merging events stem from the creation of new connections between individuals in the network.…”

Section: Epicnetmentioning

confidence: 99%

“…Communities. In the real world, an individual is a part of one or more communities 49 and this phenomenon is portrayed in the networks generated by EpiCNet. Figure 4a suggests that most of the individuals are in fewer than 20 communities with more than half of them in at most 10 communities.…”

Section: Number Of Individuals and Connectionsmentioning

confidence: 99%

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A time evolving online social network generation algorithm

Shirzadian

Antony²,

Gattani³

et al. 2023

Sci Rep

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The rapid growth of online social media usage in our daily lives has increased the importance of analyzing the dynamics of online social networks. However, the dynamic data of existing online social media platforms are not readily accessible. Hence, there is a necessity to synthesize networks emulating those of online social media for further study. In this work, we propose an epidemiology-inspired and community-based, time-evolving online social network generation algorithm (EpiCNet), to generate a time-evolving sequence of random networks that closely mirror the characteristics of real-world online social networks. Variants of the algorithm can produce both undirected and directed networks to accommodate different user interaction paradigms. EpiCNet utilizes compartmental models inspired by mathematical epidemiology to simulate the flow of individuals into and out of the online social network. It also employs an overlapping community structure to enable more realistic connections between individuals in the network. Furthermore, EpiCNet evolves the community structure and connections in the simulated online social network as a function of time and with an emphasis on the behavior of individuals. EpiCNet is capable of simulating a variety of online social networks by adjusting a set of tunable parameters that specify the individual behavior and the evolution of communities over time. The experimental results show that the network properties of the synthetic time-evolving online social network generated by EpiCNet, such as clustering coefficient, node degree, and diameter, match those of typical real-world online social networks such as Facebook and Twitter.

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Section: Number Of Individuals and Connectionssupporting

confidence: 89%

Section: Preliminariesmentioning

confidence: 99%

Section: Epicnetmentioning

confidence: 99%

Section: Number Of Individuals and Connectionsmentioning

confidence: 99%

See 2 more Smart Citations

A time evolving online social network generation algorithm

Shirzadian

Antony²,

Gattani³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…In addition, to interpret the network analysis, the degree of centrality is measured by using concepts such as the node, link, and connection degree [ 32 ]. Centrality is an index expressing the degree to which an actor is centrally located in the entire network; through centrality analysis, it is possible to identify key actors in the network and to determine how close each actor is to the center, along with similar metrics [ 33 ]. By showing the position each actor (nodes, keywords) occupies in the overall network and mathematically presenting their size, the actors can be separated into the core part and the periphery of the network.…”

Section: Methodsmentioning

confidence: 99%

Digital Health Care Industry Ecosystem: Network Analysis

Park¹,

Park²,

Lee³

2022

J Med Internet Res

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Background As the need for digital health care based on mobile devices is increasing, with the rapid development of digital technologies, especially in the face of the COVID-19 pandemic, gaining a better understanding of the industrial structure is needed to activate the use of digital health care. Objective The aim of this study was to suggest measures to revitalize the digital health care industry by deriving the stakeholders and major issues with respect to the ecosystem of the industry. Methods A total of 1822 newspaper articles were collected using Big Kings, a big data system for news, for a limited period from 2016 to August 2021, when the mobile health care project was promoted in Korea centered on public health centers. The R and NetMiner programs were used for network analysis. Results The Korean government and the Ministry of Health and Welfare showed the highest centrality and appeared as major stakeholders, and their common major issues were “reviewing the introduction of telemedicine,” “concerns about bankruptcy of local clinics,” and “building an integrated platform for precision medicine.” In addition, the major stakeholders of medical institutions and companies were Seoul National University Hospital, Kangbuk Samsung Hospital, Ajou University Hospital, Samsung, and Vuno Inc. Conclusions This analysis confirmed that the issues related to digital health care are largely composed of telemedicine, data, and health care business. For digital health care to develop as a national innovative growth engine and to be institutionalized, the development of a digital health care fee model that can improve the regulatory system and the cost-effectiveness of patient care, centering on the Ministry of Health and Welfare as a key stakeholder, is essential.

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The evaluation of community detection techniques on real-world networks

Kumar,

Singh

2024

Soc. Netw. Anal. Min.

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A review on community structures detection in time evolving social networks

Cited by 18 publications

References 39 publications

A time evolving online social network generation algorithm

A time evolving online social network generation algorithm

Digital Health Care Industry Ecosystem: Network Analysis

The evaluation of community detection techniques on real-world networks

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