Skip to content
  • Categories
  • Recent
  • Tags
  • Popular
  • World
  • Paper Copilot
  • OpenReview.net
  • Deadlines
  • CSRanking
  • AI Reviewer: coming soon ...
Skins
  • Light
  • Cerulean
  • Cosmo
  • Flatly
  • Journal
  • Litera
  • Lumen
  • Lux
  • Materia
  • Minty
  • Morph
  • Pulse
  • Sandstone
  • Simplex
  • Sketchy
  • Spacelab
  • United
  • Yeti
  • Zephyr
  • Dark
  • Cyborg
  • Darkly
  • Quartz
  • Slate
  • Solar
  • Superhero
  • Vapor

  • Default (No Skin)
  • No Skin
Collapse
CSPaper

CSPaper: review sidekick

CSPaper AI Reviewer: coming soon ...
  1. Home
  2. Peer Review in Computer Science: good, bad & broken
  3. Data Mining & Database
  4. KDD 2025 2nd-round Review Results: How Did Your Paper Do?

KDD 2025 2nd-round Review Results: How Did Your Paper Do?

Scheduled Pinned Locked Moved Data Mining & Database
kdd2025rebuttal
26 Posts 11 Posters 2.6k Views
  • Oldest to Newest
  • Newest to Oldest
  • Most Votes
Reply
  • Reply as topic
Log in to reply
This topic has been deleted. Only users with topic management privileges can see it.
  • M magicparrots

    A data point:

    GNN work, got

    Novelty: 3, 2, 2, 3, 2
    Technical Quality: 2, 2, 2, 2, 2
    Confidence: 3, 4, 3, 4, 4

    Need to rebuttal? anyone knows more? 2 weeks challenge ahead!

    lelecaoL Offline
    lelecaoL Offline
    lelecao
    Super Users
    wrote on last edited by
    #9

    @magicparrots

    So sorry to hear that — sounds like a solid paper.

    For my case,
    One reviewer gave two 2s just because they didn’t see the value of improving efficiency or where it would be useful, even though that’s the whole point of many ML contributions. Another reviewer didn’t understand the paper and asked for line-by-line comments on pseudocode. That’s just disheartening.

    Also noticed each review response is limited to 2500 characters. Does anyone know if we can reply in multiple stacked comments?

    1 Reply Last reply
    2
    • Kevin CrisK Offline
      Kevin CrisK Offline
      Kevin Cris
      wrote on last edited by
      #10

      https://www.zhihu.com/question/12035973262/answers/updated
      some data points from Chinese researcher community

      1 Reply Last reply
      1
      • H Offline
        H Offline
        Hu8kKo34
        Super Users
        wrote on last edited by
        #11

        Anyone knows the likelihood of an NLP (LLM agent and its evaluation on many public datasets) work accepted to KDD, either main or applied data science track?

        1 Reply Last reply
        0
        • Nilesh VermaN Offline
          Nilesh VermaN Offline
          Nilesh Verma
          wrote on last edited by
          #12

          what are the chances of acceptance in KDD feb, here is my score

          Relevance: 3.5 (based on 4, 3, 4, 3, 4)
          Novelty: 3.0 (based on 4, 3, 2, 3, 2)
          Technical Quality: 3.0 (based on 3, 3, 3, 3, 3)
          Presentation: 2.8 (based on 3, 3, 3, 2, 3)
          Reproducibility: 3.0 (based on 3, 3, 3, 3, 3)
          Reviewer Confidence: 3.4 (based on 3, 4, 3, 4, 3)

          SylviaS Hsi Ping LiH 2 Replies Last reply
          1
          • Nilesh VermaN Nilesh Verma

            what are the chances of acceptance in KDD feb, here is my score

            Relevance: 3.5 (based on 4, 3, 4, 3, 4)
            Novelty: 3.0 (based on 4, 3, 2, 3, 2)
            Technical Quality: 3.0 (based on 3, 3, 3, 3, 3)
            Presentation: 2.8 (based on 3, 3, 3, 2, 3)
            Reproducibility: 3.0 (based on 3, 3, 3, 3, 3)
            Reviewer Confidence: 3.4 (based on 3, 4, 3, 4, 3)

            SylviaS Offline
            SylviaS Offline
            Sylvia
            Super Users
            wrote on last edited by root
            #13

            @Nilesh-Verma from what I hear, Novelty and TQ (combined with confidence) are two most important dimension for making the final decision. I think TQ scores are pretty good; Novelty scores are not bad either. If rebuttal can increase one of the "2"s to 3, then the chance of getting an acceptance will be even higher.

            1 Reply Last reply
            0
            • rootR Offline
              rootR Offline
              root
              wrote on last edited by root
              #14

              I hereby paste the historical acceptance rate of KDD research tracks

              Conference Long Paper Acceptance Rate
              KDD'14 14.6% (151/1036)
              KDD'15 19.5% (160/819)
              KDD'16 13.7% (142/1115)
              KDD'17 17.4% (130/748)
              KDD'18 18.4% (181/983) (107 orals and 74 posters)
              KDD'19 14.2% (170/1200) (110 orals and 60 posters)
              KDD'20 16.9% (216/1279)
              KDD'22 15.0% (254/1695)
              KDD'23 22.1% (313/1416)
              KDD'24 20.0% (411/2046)

              Note that KDD'24 accepted 151 ADS track papers from 738 submissions!

              1 Reply Last reply
              0
              • SylviaS Offline
                SylviaS Offline
                Sylvia
                Super Users
                wrote on last edited by
                #15

                The KDD PC just opened the comment phase until Apr 18 (AoE). You can respond to reviewer follow-ups or raise concerns to AC/SAC via the Official Comment button.

                ⚠️ A few don’ts:

                • No URLs — they’ll auto-delete your comment.
                • No bypassing rebuttal limits — don’t treat comments as extra rebuttal space.
                • Don’t badger reviewers — 1 ping is enough.
                • Stay respectful — tone matters.

                Good luck everyone 🤞

                1 Reply Last reply
                1
                • riverR river

                  I made a summary of data points from KDD 2025 1st round results:

                  Novelty Scores Technical Quality Scores Confidence Scores Rebuttal Outcome Final Decision Notes
                  3 3 3 3 3 3 4 3 3 2 3 2 – Addressed issues ✅ Accepted "Rebuttal is so difficult with all the twists and turns"
                  2 2 3 2 2 3 3 2 2 3 3 3 3 3 3 Submitted ❌ Rejected "Can I just run away?"
                  4 3 3 1 4 4 2 2 – Explained issues ❌ Rejected "Large variance across reviewers; no score changes post-rebuttal"
                  3 3 3 3 3 2 – Unsure 🟡 Unknown "Still considering rebuttal; not sure if it's worth the effort"
                  3 3 3 3 3 3 3 3 3 3 3 2 – Minor clarifications ✅ Accepted "Final scores unchanged but accepted after positive AC decision"
                  3 4 3 3 3 3 2 2 3 2 2 3 – Clarified results ❌ Rejected "Novelty OK, but TQ too weak; didn't convince reviewers"
                  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Submitted ✅ Accepted "Strong consensus; one of the smoother cases"
                  3 3 3 3 3 2 – No rebuttal ❌ Rejected "No rebuttal submitted; borderline scores"
                  3 3 2 2 3 3 2 2 – Rebuttal sent ❌ Rejected "Reviewers did not change their opinion"
                  3 3 3 3 3 3 3 3 3 3 2 2 – Rebuttal helped ✅ Accepted "Accepted despite one weaker reviewer"
                  3 3 3 3 3 3 3 3 3 3 3 3 Rebuttal sent 🟡 Unknown "In limbo; waiting for final decision"
                  3 3 3 3 2 2 2 2 – Not convincing ❌ Rejected "Work deemed not ‘KDD-level’ despite rebuttal"
                  3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Submitted ✅ Accepted "Perfectly consistent reviewers; smooth acceptance"
                  3 3 3 2 3 3 2 2 – Rebuttal failed ❌ Rejected "Low technical quality and variance led to rejection"

                  📌 Note: Data sourced from community discussions on Zhihu, Reddit, and OpenReview threads. Subject to sample bias.

                  Hsi Ping LiH Offline
                  Hsi Ping LiH Offline
                  Hsi Ping Li
                  wrote on last edited by Hsi Ping Li
                  #16

                  @river Hi river,

                  Excuse me, do you know if these scores are the final scores after the rebuttal? Really appreciate it if you could provide more information about this 🙂

                  riverR 1 Reply Last reply
                  0
                  • Nilesh VermaN Nilesh Verma

                    what are the chances of acceptance in KDD feb, here is my score

                    Relevance: 3.5 (based on 4, 3, 4, 3, 4)
                    Novelty: 3.0 (based on 4, 3, 2, 3, 2)
                    Technical Quality: 3.0 (based on 3, 3, 3, 3, 3)
                    Presentation: 2.8 (based on 3, 3, 3, 2, 3)
                    Reproducibility: 3.0 (based on 3, 3, 3, 3, 3)
                    Reviewer Confidence: 3.4 (based on 3, 4, 3, 4, 3)

                    Hsi Ping LiH Offline
                    Hsi Ping LiH Offline
                    Hsi Ping Li
                    wrote on last edited by
                    #17

                    @Nilesh-Verma Hi Nilesh, I am sure the scores of your paper are higher than those of most authors. Congs. Besides, did your reviewers increase their ratings for your paper in the rebuttal process?

                    1 Reply Last reply
                    1
                    • Hsi Ping LiH Hsi Ping Li

                      @river Hi river,

                      Excuse me, do you know if these scores are the final scores after the rebuttal? Really appreciate it if you could provide more information about this 🙂

                      riverR Offline
                      riverR Offline
                      river
                      wrote on last edited by
                      #18

                      @Hsi-Ping-Li

                      This the best effort scores, meaning I take the latest available scores reported in the community. If they are updated by the authors after rebuttal, then I take that, otherwise I would assume the scores did not change.

                      For the data points with accept/reject outcome, I think all of them are post-rebuttal scores.

                      Hsi Ping LiH 1 Reply Last reply
                      1
                      • M magicparrots

                        A data point:

                        GNN work, got

                        Novelty: 3, 2, 2, 3, 2
                        Technical Quality: 2, 2, 2, 2, 2
                        Confidence: 3, 4, 3, 4, 4

                        Need to rebuttal? anyone knows more? 2 weeks challenge ahead!

                        Hsi Ping LiH Offline
                        Hsi Ping LiH Offline
                        Hsi Ping Li
                        wrote on last edited by
                        #19

                        @magicparrots

                        Hi magicparrots!

                        did the reviewers raise their scores for your paper after the rebuttal process?
                        I also submitted a paper about GNN, and only one reviewer out of five raised 1 score for my paper 😞

                        1 Reply Last reply
                        0
                        • riverR river

                          @Hsi-Ping-Li

                          This the best effort scores, meaning I take the latest available scores reported in the community. If they are updated by the authors after rebuttal, then I take that, otherwise I would assume the scores did not change.

                          For the data points with accept/reject outcome, I think all of them are post-rebuttal scores.

                          Hsi Ping LiH Offline
                          Hsi Ping LiH Offline
                          Hsi Ping Li
                          wrote on last edited by
                          #20

                          @river Many thanks for your details! 🙂

                          1 Reply Last reply
                          0
                          • rootR Offline
                            rootR Offline
                            root
                            wrote on last edited by
                            #21

                            Stats from official email:

                            The Research Track of KDD 2025 (February Cycle) received 1988 submissions, with an overall acceptance rate of ~18.4%. All submissions received at least three reviews, while most had four or five. Area Chairs provided meta-reviews and preliminary recommendations, which were deliberated further by the Senior Area Chairs and decided on by the Program Chairs.

                            ...

                            A submission rejected from the Research Track may not be resubmitted within 12 months to the KDD Research Track (i.e., the earliest resubmission date of your paper to the KDD research track is February 2026).

                            1 Reply Last reply
                            0
                            • JoanneJ Offline
                              JoanneJ Offline
                              Joanne
                              wrote on last edited by
                              #22

                              Thanks for the information. Especially the resubmission restriction. Something to watch out for when planning next steps.

                              1 Reply Last reply
                              0
                              • JoanneJ Offline
                                JoanneJ Offline
                                Joanne
                                wrote on last edited by
                                #23

                                KDD 2025 (February Cycle) – What the Score Patterns Reveal

                                After combing through 22 self-reported results, three consistent patterns jump out:

                                • All-3’s are not lethal. Several papers with a flat 3-3 profile survived because nobody down-voted hard and the Area Chair (AC) was on their side.
                                • 4–2 vs 3–3 is still a coin-flip. A spiky 4–2 pair can trump steady 3–3s, yet clean consistency sometimes wins when the AC trusts uniform support.
                                • Reviewer kindness matters. A single upgrade (e.g., Technical 3 → 4) in the last round carried borderline submissions over the line.

                                Who Actually Got In? – Mini Score Sheet

                                Alias Final Mean (N / T) Earlier Lows Verdict
                                author 1 #1 3.6 / 4.0 early 3-3-4 mix ✅ Accept
                                author 1 #2 3.6 / 3.4 weaker T ✅ Accept
                                author 2 ≈ 3.2 / 2.8 one reviewer gave 2 / 2 ✅ Accept — “kind-hearted AC”
                                author 3 3.0 / 3.0 flat all-3’s ✅ Accept
                                author 4 3.0 / 3.0 two negative votes (2 / 2) ✅ Accept
                                author 5 3.4 / 4.0 T started 3-3-2-2-2 ✅ Accept — generous reviewer bumped T to 4

                                Messages from this Small Sample

                                1. ≈ 3.0 averages can pass — the AC’s veto (positive or negative) is the real gatekeeper.
                                2. One low score plus a confident critique can still sink you — numbers alone aren’t everything.
                                3. Polite, point-by-point rebuttals can move scores, though not as often as we’d like.

                                How's your scores? We will make a new pattern after you share with us your.

                                C 1 Reply Last reply
                                1
                                • JoanneJ Joanne

                                  KDD 2025 (February Cycle) – What the Score Patterns Reveal

                                  After combing through 22 self-reported results, three consistent patterns jump out:

                                  • All-3’s are not lethal. Several papers with a flat 3-3 profile survived because nobody down-voted hard and the Area Chair (AC) was on their side.
                                  • 4–2 vs 3–3 is still a coin-flip. A spiky 4–2 pair can trump steady 3–3s, yet clean consistency sometimes wins when the AC trusts uniform support.
                                  • Reviewer kindness matters. A single upgrade (e.g., Technical 3 → 4) in the last round carried borderline submissions over the line.

                                  Who Actually Got In? – Mini Score Sheet

                                  Alias Final Mean (N / T) Earlier Lows Verdict
                                  author 1 #1 3.6 / 4.0 early 3-3-4 mix ✅ Accept
                                  author 1 #2 3.6 / 3.4 weaker T ✅ Accept
                                  author 2 ≈ 3.2 / 2.8 one reviewer gave 2 / 2 ✅ Accept — “kind-hearted AC”
                                  author 3 3.0 / 3.0 flat all-3’s ✅ Accept
                                  author 4 3.0 / 3.0 two negative votes (2 / 2) ✅ Accept
                                  author 5 3.4 / 4.0 T started 3-3-2-2-2 ✅ Accept — generous reviewer bumped T to 4

                                  Messages from this Small Sample

                                  1. ≈ 3.0 averages can pass — the AC’s veto (positive or negative) is the real gatekeeper.
                                  2. One low score plus a confident critique can still sink you — numbers alone aren’t everything.
                                  3. Polite, point-by-point rebuttals can move scores, though not as often as we’d like.

                                  How's your scores? We will make a new pattern after you share with us your.

                                  C Offline
                                  C Offline
                                  cocktailfreedom
                                  Super Users
                                  wrote on last edited by
                                  #24

                                  @Joanne said in KDD 2025 2nd-round Review Results: How Did Your Paper Do?:

                                  KDD 2025 (February Cycle) – What the Score Patterns Reveal

                                  After combing through 22 self-reported results, three consistent patterns jump out:

                                  • All-3’s are not lethal. Several papers with a flat 3-3 profile survived because nobody down-voted hard and the Area Chair (AC) was on their side.
                                  • 4–2 vs 3–3 is still a coin-flip. A spiky 4–2 pair can trump steady 3–3s, yet clean consistency sometimes wins when the AC trusts uniform support.
                                  • Reviewer kindness matters. A single upgrade (e.g., Technical 3 → 4) in the last round carried borderline submissions over the line.

                                  Who Actually Got In? – Mini Score Sheet

                                  Alias Final Mean (N / T) Earlier Lows Verdict
                                  author 1 #1 3.6 / 4.0 early 3-3-4 mix ✅ Accept
                                  author 1 #2 3.6 / 3.4 weaker T ✅ Accept
                                  author 2 ≈ 3.2 / 2.8 one reviewer gave 2 / 2 ✅ Accept — “kind-hearted AC”
                                  author 3 3.0 / 3.0 flat all-3’s ✅ Accept
                                  author 4 3.0 / 3.0 two negative votes (2 / 2) ✅ Accept
                                  author 5 3.4 / 4.0 T started 3-3-2-2-2 ✅ Accept — generous reviewer bumped T to 4

                                  Messages from this Small Sample

                                  1. ≈ 3.0 averages can pass — the AC’s veto (positive or negative) is the real gatekeeper.
                                  2. One low score plus a confident critique can still sink you — numbers alone aren’t everything.
                                  3. Polite, point-by-point rebuttals can move scores, though not as often as we’d like.

                                  How's your scores? We will make a new pattern after you share with us your.

                                  Thanks for sharing! mine got rejected though -- mean T score 2.5-ish

                                  1 Reply Last reply
                                  0
                                  • JoanneJ Offline
                                    JoanneJ Offline
                                    Joanne
                                    wrote on last edited by
                                    #25

                                    @cocktailfreedom Thanks for sharing that and sorry to hear about the rejection. A 2.5 mean T score definitely stings, but it says nothing about your potential or the value of your work long term. Peer review can be noisy, biased, or just not aligned with where your idea fits best.
                                    Let me share Saining Xie's comment “I wouldn’t call conferences a lottery, but a bit of perseverance does go a long way.”

                                    1 Reply Last reply
                                    0
                                    • rootR Offline
                                      rootR Offline
                                      root
                                      wrote last edited by
                                      #26

                                      The early bird deadline is June 18th! Register on or before the deadline to receive discounted rates for KDD 2025! 😊

                                      1 Reply Last reply
                                      0
                                      Reply
                                      • Reply as topic
                                      Log in to reply
                                      • Oldest to Newest
                                      • Newest to Oldest
                                      • Most Votes


                                      • 1
                                      • 2
                                      • Login

                                      • Don't have an account? Register

                                      • Login or register to search.
                                      © 2025 CSPaper.org Sidekick of Peer Reviews
                                      Debating the highs and lows of peer review in computer science.
                                      • First post
                                        Last post
                                      0
                                      • Categories
                                      • Recent
                                      • Tags
                                      • Popular
                                      • World
                                      • Paper Copilot
                                      • OpenReview.net
                                      • Deadlines
                                      • CSRanking
                                      • AI Reviewer: coming soon ...