kdd-writing-style

KDD Writing Style

Use this during revision passes. KDD prose has a recognizable register: it talks about data regimes (scale, drift, sparsity, heterogeneity, label scarcity) rather than model families, it attaches every performance adjective to a mechanism, and it treats dataset cardinalities as part of grammar — a dataset without a size reads as unfinished. The resources/worked-examples/01-introduction.md file shows a full before/after; this skill is the rule set behind it.

First-page contract

Within page one, a KDD reviewer expects to find, in some order:

1. The data regime that makes the problem hard (not "X is important").
2. Why existing method families fail in that regime, each for a mechanism-level reason.
3. The named mechanism this paper adds (a primitive someone could re-implement).
4. Evidence scoped with numbers: dataset scale, throughput or memory if claimed, headline quality delta.
5. For ADS: where this is deployed and the post-launch headline number.

If the introduction could open an ICML or a database paper unchanged, the framing is not yet KDD's.

Register rules

Contribution bullets that survive review

Circular bullets ("we propose X, we evaluate X") waste the most-read lines of the paper. Each bullet should assert a falsifiable fact:

Weak:   - We propose StreamHive, a novel framework for stream anomaly detection.
Strong: - We show bounded-memory detection under drift reduces to online decay-rate
          selection, and give a mixture-of-sketches scheme with O(1) update cost.

Weak:   - Extensive experiments demonstrate the effectiveness of our approach.
Strong: - Across three streams up to 2.1B events, the scheme matches window-retrained
          deep baselines on AUPRC at fixed 512MB memory; ablations attribute the gain
          to the drift weighting rather than the ensemble.

ADS voice

The Applied Data Science register differs deliberately from the Research register:

• Lead with the business/operational problem and the deployment context, then the technical path — reviewers of ADS papers score problem realism before novelty.
• Lessons learned are content, not filler: what failed before the shipped design, which offline metrics mispredicted online behavior, what broke at rollout. The classic KDD applied papers are remembered for exactly these sections.
• Post-launch numbers must be flagged as such and separated from offline evaluation — the track's desk-reject rule is about quantified post-launch performance, so make those numbers typographically impossible to miss.

Two-column compression tactics

The ACM sigconf format is tight, and submission is 8 content pages:

• Write display math sparingly; inline the one-off definitions and reserve display lines for objects the paper reuses.
• Every figure earns its column-width: delete any plot whose caption cannot state what decision it supports. Wide tables go table* (full width) early, since late layout flips cascade page breaks.
• Algorithm environments are expensive; one algorithm block for the core mechanism, prose for variants, full pseudocode in the appendix.
• Kill roadmap paragraphs ("Section 2 discusses...") — in an 8-page paper the structure is visible without a tour guide.
• The camera-ready adds exactly one content page; do not defer required content to it (reviewers score the submission, and refs+appendix get capped at 3 pages later).

Anonymity phrasing

• Research Track: "our production system at a large e-commerce platform" is fine; naming the company usually is not — and internal system codenames are as identifying as the company name.
• Cite your own prior work in third person, and check the referenced repository's README carries no author trace (kdd-artifact-evaluation).

Title and abstract mechanics

• KDD titles favor named-system-plus-claim (": ") or a direct claim; question titles and pun-only titles underperform with this reviewer pool.
• The abstract is bid-bait: reviewers choose papers from it, so the regime vocabulary (graph, stream, drift, recommendation, fraud, spatio-temporal) must appear honestly — the wrong vocabulary buys the wrong experts.
• One quantitative claim in the abstract, minimum: an abstract with no number is a style violation at a venue whose currency is measured evidence.

Revision pass order

A concrete sequence for turning a complete draft into a KDD submission, one pass per day in the final week:

1. Regime pass: rewrite page one until the data regime leads; fix the abstract's vocabulary and number.
2. Adjective audit: grep the draft for "novel", "significantly", "efficient", "large-scale", "real-world"; each occurrence either gains a mechanism/number or dies.

grep -n -iE "novel|significant|efficient|large-scale|real-world|extensive" \
  sections/*.tex | wc -l   # target: near zero unattached occurrences

3. Claim-evidence pass: every contribution bullet cross-referenced to its table, figure, or section; circular bullets rewritten as assertions.
4. Compression pass: apply the two-column tactics until the body sits at 8 pages without spacing hacks (kdd-submission treats those as desk-level).
5. Anonymity pass: self-citations, system codenames, acknowledgements, repo traces — last, so later edits cannot reintroduce leaks.

Output format

[Register diagnosis] regime-first / model-first (needs reframe) / journal-paced
[First-page contract] items present: <1-5 checklist>
[Adjective audit] <unattached efficiency/scale adjectives found>
[Bullet quality] assertive / circular -> <rewrites>
[ADS voice] lessons-learned present / post-launch numbers flagged / N-A
[Compression cuts] <move/delete/merge list to reach 8 pages>