Community detection based on human social behavior

Sheng, Jinfang; Hu, Jie; Sun, Zejun; Wang, Bin; Ullah, Aman; Wang, Kai; Zhang, Junkai

doi:10.1016/j.physa.2019.121765

Cited by 12 publications

(2 citation statements)

References 40 publications

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“…Especially, community detection involves the use of some optimization strategy for transforming a large-scale complex network into a set of disjoint and compact subgroups, without prior knowledge about the number of subgroups and their sizes. Several algorithms for community detection have been developed, combining techniques and tools from different disciplines such as biology [55], physics [56], statistics [57], social sciences [58][59][60], cognitive sciences [61], mathematics [62], economics [63], and computer sciences [64]. It is commonly acknowledged that there is no unique community detection algorithm that can universally accommodate all kinds of social networks with high accuracy because of the discrepancy in network types and purposes.…”

Section: Community Detection Algorithmsmentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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Over the past few years, the number and volume of data sources in healthcare databases has grown exponentially. Analyzing these voluminous medical data is both opportunity and challenge for knowledge discovery in health informatics. In the last decade, social network analysis techniques and community detection algorithms are being used more and more in scientific fields, including healthcare and medicine. While community detection algorithms have been widely used for social network analysis, a comprehensive review of its applications for healthcare in a way to benefit both health practitioners and the health informatics community is still overwhelmingly missing. This paper contributes to fill in this gap and provide a comprehensive and up-to-date literature research. Especially, categorizations of existing community detection algorithms are presented and discussed. Moreover, most applications of social network analysis and community detection algorithms in healthcare are reviewed and categorized. Finally, publicly available healthcare datasets, key challenges, and knowledge gaps in the field are studied and reviewed.

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Section: Community Detection Algorithmsmentioning

confidence: 99%

Community Detection Algorithms in Healthcare Applications: A Systematic Review

et al. 2023

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“…UmbcBlog network [ 35 ]: This network contains 2382 edges and 404 nodes, including two real community structures.…”

Section: Experimental Evaluationmentioning

confidence: 99%

Research on Community Detection in Complex Networks Based on Internode Attraction

Sheng

Liu

Chen

et al. 2020

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With the rapid development of computer technology, the research on complex networks has attracted more and more attention. At present, the research directions of cloud computing, big data, internet of vehicles, and distributed systems with very high attention are all based on complex networks. Community structure detection is a very important and meaningful research hotspot in complex networks. It is a difficult task to quickly and accurately divide the community structure and run it on large-scale networks. In this paper, we put forward a new community detection approach based on internode attraction, named IACD. This algorithm starts from the perspective of the important nodes of the complex network and refers to the gravitational relationship between two objects in physics to represent the forces between nodes in the network dataset, and then perform community detection. Through experiments on a large number of real-world datasets and synthetic networks, it is shown that the IACD algorithm can quickly and accurately divide the community structure, and it is superior to some classic algorithms and recently proposed algorithms.

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