Developing Efficient Algorithms for Large-Scale Graph Processing in Social Networks

Abstract

The explosive growth of social networks—now exceeding 19 billion nodes and 1.25 trillion edges across major platforms—has created unprecedented computational challenges for graph analytics. Existing distributed graph processing frameworks suffer from excessive inter-machine communication, poor data locality, load imbalance caused by power-law degree distributions, and inability to efficiently handle dynamic graph updates. This paper presents SocioGraph, a distributed graph processing framework specifically optimized for social network analytics. SocioGraph introduces three key innovations: (1) a community-aware graph partitioning algorithm that exploits the inherent community structure of social networks to minimize edge cuts by 69% compared to hash-based partitioning, reducing inter-node communication volume by up to 72%; (2) an adaptive message combining protocol that aggregates vertex messages hierarchically based on community boundaries, reducing network traffic by 65% for iterative algorithms; and (3) an incremental computation engine that processes dynamic graph updates (edge insertions/deletions, new vertices) without full recomputation, achieving 12–45× speedup over batch reprocessing for streaming social network data. We evaluate SocioGraph on five real-world and synthetic social network datasets ranging from 4.8 million to 1 billion vertices, across five canonical graph algorithms (PageRank, BFS, Connected Components, Community Detection, Triangle Counting). SocioGraph achieves 3.4–6.6× speedup over Pregel, 2.5–5.4× over GraphX, and 1.7–3.1× over PowerGraph, while reducing peak memory consumption by 62–76% and demonstrating near-linear scalability up to 128 machines.