SIGKDD 2025 deadline
ACM Knowledge Discovery and Data Mining
August 3-7, 2025 · Toronto, ON, Canada
All deadlines for this edition have passed.
Key dates
first round: Abstract / registration
Thu, Aug 1, 2024 · 23:59 AoE
first round: Full paper
Thu, Aug 8, 2024 · 23:59 AoE
second round: Abstract / registration
Mon, Feb 3, 2025 · 23:59 AoE
second round: Full paper
Mon, Feb 10, 2025 · 23:59 AoE
Conference
August 3-7, 2025 · Toronto, ON, Canada
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