A Scalable Distributed Graph Processing Framework for Large-Scale Social Network Analysis

International Journal of Computer Science and Technology | Vol. 13, No. 6, June 2011 | pp. 36–42
DOI: 10.46882/2011/IJCST/000112

Research Article

Title: A Scalable Distributed Graph Processing Framework for Large-Scale Social Network Analysis

Names of Authors: Alexander J. Wright¹, Choi Min-Soo²

Authors’ Affiliations: ¹School of Computer Science, Georgia Institute of Technology, Atlanta, USA; ²Department of Computer Engineering, KAIST, Daejeon, South Korea

Abstract: Large-scale digital social networks generate immense relational graphs containing billions of vertices and directed edges, severely testing the memory boundaries of monolithic database engines. Distributed computing architectures must partition these massive graphs effectively while minimizing inter-node communication network overhead during parallel traversals. This study describes a Scalable Distributed Graph Processing Framework (SDGPF) that implements a vertex-centric parallel computational model paired with a 2D balance partition mechanism. The proposed execution engine utilizes an asynchronous message-passing interface to allow independent node computation during PageRank iterations and single-source shortest path operations. Benchmark evaluations conducted over a cluster of 16 virtual machines processing the LiveJournal dataset showed a 43.5% reduction in graph partitioning execution times compared to traditional hash-based data distribution strategies. Network cross-talk dropped significantly, maintaining an execution throughput of 2.4 * 10^6 edges processed per second. The system demonstrated near-linear horizontal speedup scaling up to maximum thread capacity, confirming its industrial applicability for real-time recommendation engines, community detection pipelines, and structural trace tracking in commercial social media environments.

Keywords: Distributed Computing, Graph Processing, Big Data Analytics, Social Networks, PageRank, Parallel Architectures

Manuscript Timeline: Received March 18, 2011; Revised May 02, 2011; Accepted May 21, 2011; Published June 26, 2011.

Citation: Wright, A. J., & Min-Soo, C. (2011). A Scalable Distributed Graph Processing Framework for Large-Scale Social Network Analysis. International Journal of Computer Science and Technology, 13(6), 36–42.

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