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kdd-writing-style
GitHub指导KDD论文修订，强调数据范式、机制命名及量化证据。涵盖首段契约、去模糊化表达、贡献点撰写及ADS风格，旨在提升论文在规模、效率和可复现性方面的专业度与说服力。
Trigger Scenarios
修改KDD论文初稿
优化引言和贡献点描述
将通用学术风格转为KDD特定注册风格
Install
npx skills add brycewang-stanford/Awesome-Journal-Skills --skill kdd-writing-style -g -y
SKILL.md
Frontmatter
{
"name": "kdd-writing-style",
"description": "Use when revising a KDD paper into the venue's register, where the first page names a data regime, a mechanism, and scale evidence, efficiency adjectives trace to design decisions, and two-column sigconf pages punish sprawl. Covers Research vs ADS voice, dataset-size discipline, assertive contribution bullets, and 8-page compression."
}
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:
- The data regime that makes the problem hard (not "X is important").
- Why existing method families fail in that regime, each for a mechanism-level reason.
- The named mechanism this paper adds (a primitive someone could re-implement).
- Evidence scoped with numbers: dataset scale, throughput or memory if claimed, headline quality delta.
- 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
| Draft habit | KDD-register rewrite | Why it matters here |
|---|---|---|
| "novel framework" | Name the mechanism: "drift-weighted sketch family" | Frameworks are unreviewable; mechanisms are ablatable |
| "large-scale experiments" | "3 graphs, 10M-2.1B edges" | Scale is the venue's currency; unquantified scale reads as small |
| "efficient" | "O(1) update; 1.4M events/s on one core" | Efficiency claims must be attributable and checkable |
| "significantly outperforms" | "+3.3 AUPRC, median of 5 seeds, IQR ±0.4" | Practitioner reviewers distrust unquantified superlatives |
| "real-world data" | Name the datasets and their provenance | "Real-world" without provenance signals toy benchmarks |
| "can be applied to many domains" | One demonstrated transfer, or silence | Unpaid generality checks are a known reject pattern |
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 ("<Name>:
") 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:
- Regime pass: rewrite page one until the data regime leads; fix the abstract's vocabulary and number.
- 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
- Claim-evidence pass: every contribution bullet cross-referenced to its table, figure, or section; circular bullets rewritten as assertions.
- Compression pass: apply the two-column tactics until the body sits at 8 pages
without spacing hacks (
kdd-submissiontreats those as desk-level). - 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>
Version History
- 9f86f09 Current 2026-07-19 16:56
