Graph analytics

Graph analytics is the analysis of data modeled as a graph — nodes representing entities like people, accounts, or products, and edges representing relationships between them, such as "follows," "paid," or "purchased together." It asks questions about structure and connectivity that a flat table of rows cannot easily answer, such as which entities are most influential or how two entities are connected.

The two broad families of technique are node- and structure-level metrics, such as centrality measures and community detection, and path- or relationship-level techniques, such as shortest-path search and link prediction. This differs from relational analysis over the same data: a join can answer what a customer bought, but a graph traversal more naturally answers who is two hops away from that customer through shared purchases, a question that would require an expensive multi-way join otherwise.

Graph analytics matters for fraud detection, recommendation, knowledge graph reasoning, and any domain where relationships carry as much signal as attributes, from supply chains to social networks. A common pitfall is forcing a graph model onto data that has little real relational structure, where a graph adds engineering overhead without an analytical payoff; graph analytics earns its cost when connections, not just entity attributes, drive the questions being asked, as with PageRank-style ranking over a link graph.