Graph Clustering Algorithms: Usage and Comparison

Graph clustering algorithms are powerful tools in data analysis for identifying patterns and structures within complex networks, with applications spanning social network analysis, recommendation systems, biological network analysis, and fraud detection. These algorithms, rooted in unsupervised learning, partition nodes in graphs into cohesive clusters based on shared characteristics, providing insights into both the overall graph structure and individual node relationships. Common methods include k-means clustering, hierarchical clustering, node embedding, modularity-based algorithms, and label propagation, each with unique strengths and limitations depending on factors like scalability and data stream compatibility. Domain-specific and technical considerations guide the selection of the appropriate algorithm, as exemplified by researchers using community detection for cost-effective flu transmission prevention. Memgraph, a high-performance graph database, supports real-time processing and scalability for complex data relationships, offering optimized algorithms through its MAGE library and third-party integrations for efficient graph clustering and analysis.