Graafipohjaisuus

Graafipohjaisuus is a data modeling and analysis approach that relies on graph structures to represent entities and their interrelations. It emphasizes the use of nodes and edges as the primary building blocks, enabling direct representation of relationships and paths between elements. The concept contrasts with predominantly tabular or hierarchical models, where relationships are often implicit or inferred.

Graphs can be property graphs (nodes and edges with attributes) or RDF graphs (subject–predicate–object triples). Central to graafipohjaisuus are graph databases and graph-processing frameworks that support traversal and pattern matching. Common graph metrics and algorithms include degree, betweenness and closeness centrality, community detection, shortest paths, and PageRank-like scoring. Modeling decisions affect query capabilities, performance, and schema flexibility; graafipohjaisuus typically favors evolving schemas and ad hoc querying of connected data.

Use cases span social networks, fraud detection, knowledge graphs, recommendation systems, and bioinformatics. Benefits include natural representation of complex relationships, flexible schemas, and efficient traversals for connected data. Challenges involve scalability in very large graphs, query optimization, and maintaining consistency across distributed stores. The term is widely used in Finnish IT and data science literature to describe graph-centric approaches to data management and analysis.