Technical debt is the future cost created when data or code must evolve without planned backward or forward compatibility, or when modeling choices fit poorly with database and software choices. section 8.2.4 section 8.2.2
In graph and GNN projects, reduce technical debt by making structure explicit and validating it iteratively:
- Define the graph schema and instance model before implementation, including nodes, edges, features, metadata, and constraints. section 8.2.2
- Cast the instance model into a graph database or processing system, then create tests and queries for model integrity. section 8.2.4
- Refactor through repeated query execution, result analysis, adjustment, and validation against project metrics. section 8.2.4
Recruitment graph checks
For a candidate-referral graph, tests can verify required non-null industry and job-type attributes, consistent Knows edge labels, plausible relationship counts, and unique candidate identifiers. These checks expose integrity problems before new data streams make workarounds or replacements expensive. section 8.2.4
