Analyze the Techniques of Community Detection in Social Networks and Their Applications

The World Wide Web's emergence paved the way for various social networking sites, facilitating easy communication and connection among users on a shared platform. A social network is essentially a graph comprising nodes and edges, representing users and their interactions, respectively. Common interests naturally draw people together, fostering the formation of communities within social networks. This community formation, in turn, leads to community detection, a process applicable in diverse fields such as finance, politics, marketing, education, and the medical field. This paper explores current3 algorithms for detecting communities in social networks and delves into potential algorithms utilizing deep learning for this purpose.

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Publication Details

Published
2024-11-22
DOI
https://doi.org/10.1109/aece62803.2024.10911381
Citations
1
Primary Topic
Complex Network Analysis Techniques
Type
article
Field-Weighted Citation Impact
0.28
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article

Analyze the Techniques of Community Detection in Social Networks and Their Applications

Radha Yadav, Sonia Choudhary, Seema Malik
1 citations
Complex Network Analysis Techniques
0.28
article

Analyze the Techniques of Community Detection in Social Networks and Their Applications

Radha Yadav, Sonia Choudhary, Seema Malik
article en
1 citations

Abstract

The World Wide Web's emergence paved the way for various social networking sites, facilitating easy communication and connection among users on a shared platform. A social network is essentially a graph comprising nodes and edges, representing users and their interactions, respectively. Common interests naturally draw people together, fostering the formation of communities within social networks. This community formation, in turn, leads to community detection, a process applicable in diverse fields such as finance, politics, marketing, education, and the medical field. This paper explores current3 algorithms for detecting communities in social networks and delves into potential algorithms utilizing deep learning for this purpose.

Reduced inequalities
Openalex Percentile: Top 41%
Complex Network Analysis Techniques
0.28
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