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http://localhost:8080/xmlui/handle/123456789/1971Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Malik, Seema | - |
| dc.date.accessioned | 2025-05-19T11:15:05Z | - |
| dc.date.available | 2025-05-19T11:15:05Z | - |
| dc.date.issued | 2024 | - |
| dc.identifier.uri | https://ieeexplore.ieee.org/document/10911381 | - |
| dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/1971 | - |
| dc.description.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. | en_US |
| dc.language.iso | en | en_US |
| dc.publisher | Analyze the techniques of community Detection in Social Networks and their Applications | en_US |
| dc.title | 4th International Conference on Advancement in Electronics & Communication Engineering AECE 2024 | en_US |
| dc.type | Article | en_US |
| Appears in Collections: | VSE&T | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| seema.docx | 135.92 kB | Microsoft Word XML | View/Open |
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