Datagraphing

Datagraphing is the practice of representing data through graphs and relational structures to reveal connections, patterns, and processes. The term can refer to both visual data storytelling using graph-based visualizations and the modeling of data as graph structures in databases and knowledge graphs. In visualization, datagraphing emphasizes relationships and networks among entities rather than isolated metrics.

Techniques include node-link diagrams, adjacency matrices, chord diagrams, sunburst charts, and network maps; when data are inherently relational, graph databases and graph query languages are used to query and traverse connections. Common tools include Gephi, Cytoscape, Neo4j Bloom, D3.js, and Graphviz. The workflow typically involves data extraction, cleaning, transformation, and mapping to a graph schema with nodes (entities) and edges (relationships), often with edge directions and weights.

Applications span social networks, biological pathways, transport and supply chains, knowledge graphs for search engines, and IT infrastructure mapping. Considerations include choosing the appropriate graph representation to avoid clutter, scalability, and readability; accessibility concerns such as color schemes; and privacy, bias, and ethical issues when displaying sensitive connections. Datagraphing as a discipline emphasizes clarity, reproducibility, and documentation of data provenance.