Graph Database Applications in Social Network Analysis: Unraveling Interconnected Data and Interactions
Session
Computer Science and Communication Engineering
Description
This study examines the use of graph databases in social network analysis, emphasizing the complex interactions and relationships that define these dynamic settings. Large volumes of interconnected data are produced by social networks, and traditional relational databases frequently find it difficult to effectively manage and query complex relationships. Graph databases offer a more user-friendly and effective framework for encapsulating the complexity of social interactions because they can represent entities as nodes and relationships as edges. This study investigates various graph database technologies and their effectiveness in modeling social network data, examining key use cases such as community detection, influence propagation, and sentiment analysis. Finally, this study emphasizes how important graph databases are to social network analysis. It suggests intriguing prospects for further research into how individuals interact and connect within social networks by exhibiting their useful applications and benefits.
Keywords:
complex interactions, network analysis, social connections
Proceedings Editor
Edmond Hajrizi
ISBN
978-9951-982-15-3
Location
UBT Kampus, Lipjan
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.397
Recommended Citation
Novoberdaliu, Alma and Hajrizi, Edmond, "Graph Database Applications in Social Network Analysis: Unraveling Interconnected Data and Interactions" (2024). UBT International Conference. 13.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/CS/13
Graph Database Applications in Social Network Analysis: Unraveling Interconnected Data and Interactions
UBT Kampus, Lipjan
This study examines the use of graph databases in social network analysis, emphasizing the complex interactions and relationships that define these dynamic settings. Large volumes of interconnected data are produced by social networks, and traditional relational databases frequently find it difficult to effectively manage and query complex relationships. Graph databases offer a more user-friendly and effective framework for encapsulating the complexity of social interactions because they can represent entities as nodes and relationships as edges. This study investigates various graph database technologies and their effectiveness in modeling social network data, examining key use cases such as community detection, influence propagation, and sentiment analysis. Finally, this study emphasizes how important graph databases are to social network analysis. It suggests intriguing prospects for further research into how individuals interact and connect within social networks by exhibiting their useful applications and benefits.