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 drift, with 0.1% labels) rather than only a property of models.
• There is a mining/discovery/prediction task with measurable output — not purely a learning-theory statement or a pure systems benchmark.
• Someone outside the authors' subfield could use the result on their data.

Anti-signals: novelty lives entirely in architecture or loss design (ML flagships); the contribution is query processing or storage (VLDB/SIGMOD); the core is retrieval ranking on web corpora (SIGIR/WSDM/WWW may fit better, though overlap is real).

Stage 2: the track fork

The decisive question: what is the strongest evidence you can honestly produce?

Two frequent mis-forks:

• Deployed system submitted to Research because the team fears the ADS bar: it then competes on mechanism novelty it may not have, while its actual strength (deployment evidence) earns no credit.
• Research prototype submitted to ADS with projected or offline-only numbers: desk-rejected under the post-launch quantification rule. "We could deploy it" is a Research-track sentence.
• The blocked-from-deployment exception in the ADS CFP is for documented external blockers (regulation, partner constraints), not for "we ran out of time."

Decision vignette

A team built a fraud-detection model, ran it in shadow mode for two months, and has a new graph-sampling trick inside it. Three honest papers hide here: (a) the sampling trick with ablations and public-graph scale curves → Research; (b) after full launch and a measured A/B window → ADS; (c) the labeled transaction graph, if releasable → Datasets & Benchmarks. The wrong move is one paper claiming all three, thinly. Pick the paper whose evidence exists this cycle — the dual-cycle calendar (kdd-workflow) makes "ADS next cycle, after the measurement window" a concrete plan rather than a consolation.

Neighbor-venue routing

Route AWAY from KDD when:
- contribution = new learning objective/architecture, data regime incidental
    -> NeurIPS / ICML / ICLR
- contribution = mining method, but community/deadline fit favors a sibling
    -> ICDM or SDM (same shape, different scale of venue)
- contribution = web/user-behavior/retrieval centric
    -> WWW / WSDM / SIGIR / RecSys
- contribution = data management, indexing, query execution
    -> VLDB / SIGMOD
- contribution = domain science outcome, method standard
    -> domain journal, or KDD's AI-for-Sciences track if the mining is real

If the paper misses at KDD, the reviews tell you which axis failed (kdd-review-process); a mechanism-novelty objection with solid deployment evidence often means the right venue was the ADS track all along — a track switch, not a venue switch.

Fit-signal reference

Timing interacts with routing

Because KDD runs two cycles, track choice is also schedule choice: a Research paper can target the nearest cycle, while the ADS version of the same project may need to wait for its measurement window to close (kdd-workflow). When co-authors disagree on track, the tiebreaker question is: which paper's gate evidence exists today? Projected evidence loses to existing evidence at this venue every time — the ADS desk-check makes that literal.

Commit checklist before writing

1. One sentence naming the data regime and why it defeats existing methods.
2. Track chosen from the evidence table above, and the track's specific bar quoted from the current CFP (track CFPs are separate documents — read the right one).
3. The nearest KDD-lineage ancestor identified (kdd-related-work).
4. The cycle chosen with evidence lead times honored (kdd-workflow).

Output format

[KDD-shaped] yes / no -> <redirect venue>
[Track] Research / ADS / Datasets+Benchmarks / AI4Science / BlueSky
[Evidence basis] <the strongest honest evidence available this cycle>
[Desk-check exposure] ADS post-launch quantification: satisfied / fails / N-A
[Regime sentence] <one sentence>
[Next action] <write / measure / wait a cycle / re-route>