Scalable Graph-Based Algorithms for Real-Time Analysis of Big Data in Social Networks
Mathematics and Computer Science Journal
,Volume
2024
,
Page 1-32
https://doi.org/10.18535/mcsj/v2024.01
The explosive growth of social networks has led to the generation of vast, complex datasets, necessitating advanced methods for real-time analysis. Graph-based algorithms provide a natural framework for representing and analyzing social networks, with nodes representing users and edges encapsulating interactions. However, the scale, velocity, and diversity of this data pose significant challenges for traditional analytical techniques. This paper explores scalable graph-based algorithms designed for real-time analysis of big data in social networks, focusing on innovations in parallel and distributed processing, streaming algorithms, and machine learning on graphs. Applications such as community detection, sentiment analysis, and anomaly detection are examined, demonstrating the potential of these algorithms in solving critical problems in social network analysis. Case studies highlight real-world implementations and performance metrics, shedding light on practical challenges and solutions. Finally, this work addresses open issues, including algorithmic scalability, system-level constraints, and ethical considerations, while proposing future directions to enhance the capabilities of graph-based systems for real-time insights in the evolving landscape of social networks