Distributed Graph Algorithms: From Local Data to Global Solutions

Abstract

As data scales increase, traditional centralized graph algorithms struggle to meet modern computational demands. Distributed graph algorithms, which parallelize data processing across multiple computing nodes, have significantly improved the efficiency of handling large-scale graph data. This report explores the principles, application scenarios, key technologies, and challenges of distributed graph algorithms, aiming to provide a comprehensive perspective from local data to global solutions. With the rapid development of computer networks and big data technologies, solving large-scale graph data problems has become a hot research topic. Distributed graph algorithms can solve problems without global information and offer new solutions for processing massive graph structures. This report introduces the basic concepts, key technologies, and challenges of distributed graph algorithms and discusses methods for achieving global solutions starting from local data through case analyses.