SIGKDD 2028 deadline
ACM Knowledge Discovery and Data Mining
Not announced yet. The dates below are projected from SIGKDD 2027 (same dates, one year on). They will update here when the call for papers is out.
Abstract / registration deadline
~9 months
- Anywhere on Earth
- ~Mon, Jul 19, 2027 · 23:59 AoE
- As published
- AoE
Key dates
Abstract / registration
~Mon, Jul 19, 2027 · 23:59 AoE
Full paper
~Mon, Jul 26, 2027 · 23:59 AoE
1st round of 2 total rounds
What SIGKDD reviewers will look for
KDD 2026 runs two cycles (Feb and Aug) on OpenReview with at least three reviews per paper, overseen by ACs and SACs. Reviewers write a summary, numbered strengths and weaknesses, check whether a resubmission addressed earlier reviews, pose rebuttal questions, and rate Relevance, Novelty, Technical Quality, Presentation and Reproducibility on 1–4 (High / Moderate / Low) plus confidence. ACs weigh technical merit, originality, potential impact, execution, presentation, related work, reproducibility and ethics. February submissions can receive a Resubmit decision for the next cycle.
- Overall score
- Technical Quality, 1–4 (top: “High - The paper exhibits a high level of technical quality with a rigorous and well-executed methodology and analysis. The results are highly reliable, well-supported, and thorough. The work demonstrates technical excellence and sets a high standard for quality in the field.”)
- Sub-scores
- Relevance, Novelty, Presentation, Reproducibility (1–4)
- Written fields
- Paper Summary · Paper Strengths · Paper Weaknesses · Resubmission · Questions And Suggestions For Rebuttal · Ethics Review Flag · Ethics Review Description
- Author checklist
- KDD submission requirements, 4 items
Where the bar sits. KDD 2026 Cycle 1 accepted 256 of 1,215 research-track papers (21%; KDD 2025: 18.7%). KDD has no overall score visible to authors; Novelty and Technical Quality act as the headline ratings. Shared averages of accepted papers cluster around 3 (Moderate) on each 1–4 axis, so a Technical Quality mean ≥ 3 with Novelty ≥ 3 is competitive and ~2.6–3 borderline.
LLM policy for reviewers
KDD 2026 requires any use of generative AI during reviewing to be disclosed in the review form, and forbids uploading any passage of a paper under review to a generative AI tool.
Official policy