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kdd-topic-selection
Use when deciding whether a project belongs at KDD and in which track — Research vs Applied Data Science vs Datasets and Benchmarks vs AI for Sciences — or whether it routes to ICDM, SDM, WSDM, CIKM, WWW, VLDB, or an ML flagship. Covers the deployment-evidence fork, data-regime framing, and SIGKDD fit signals before writing.
KDD-Skills/skills/kdd-topic-selection/SKILL.md
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# KDD Topic Selection
Use this before any writing. KDD routing is a **two-stage decision** — first whether
the work is KDD-shaped at all, then which track — and the second stage is the one
teams get wrong, because the Research and ADS tracks want different papers written
about the same system. A submission may enter exactly one of them per cycle.
## Stage 1: is it KDD-shaped?
KDD rewards contributions where **the data regime is the protagonist**: scale, drift,
heterogeneity, noise, sparsity, graph structure, streams, or the friction of real
deployment. Signals:
- The method's value claim references a property of data (works at 10^9 edges, under