Design and Optimization of Efficient Algorithms for Large-Scale Graph Processing in Distributed Systems
Keywords:
Graph Processing, Distributed Systems, Parallel Computing, Graph Algorithms, Big Data Analytics, Scalability, Load BalancingAbstract
Large-scale graph processing has become a fundamental requirement in modern applications such as social network analysis, recommendation systems, transportation networks, cybersecurity, and scientific computing. Traditional graph processing methods face significant challenges when dealing with massive graph datasets due to computational complexity, memory limitations, and communication overhead. This research proposes a distributed graph processing framework that focuses on the design and optimization of efficient graph algorithms for large-scale environments. The study incorporates graph partitioning, parallel computation, load balancing, and communication optimization techniques to improve processing efficiency. Experimental evaluations demonstrate improvements in execution time, scalability, resource utilization, and throughput compared with conventional graph processing approaches. The proposed framework provides a scalable solution for handling large graph datasets in distributed computing environments.
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