Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Data Science view of the KDD 2014

13,364 views

Published on

KDD is a premier conference that brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

These slides give some statistics about the KDD program and present data science view of the paper review process: 1100 submissions, 3000 reviews, and 150 accepted papers.

Published in: Data & Analytics
  • Hello! High Quality And Affordable Essays For You. Starting at $4.99 per page - Check our website! https://vk.cc/82gJD2
       Reply 
    Are you sure you want to  Yes  No
    Your message goes here

Data Science view of the KDD 2014

  1. 1. Data Science of the KDD ‘14 Review Process Jure Leskovec (Stanford) and Wei Wang (UCLA) Joint work with Jason Hirshman and David Zeng (Stanford)
  2. 2. KDD 2014 Research Track Statistics
  3. 3. KDD 2014 Program Largest KDD program ever: • 151 research papers (20% growth over KDD’13) • 43 industry & govt. papers (30% growth) • 26 workshops (75% growth) • 11 tutorials (83% growth) Program highlights: • Paper spotlights early morning (8:15am) • Oral presentations (Mon-Wed) • Posters at the reception (Tue night)
  4. 4. KDD 2014 Research Track • 1036 submissions from 2600 authors – 42% increase over KDD ’13 • 151 papers: – Acceptance rate 14.6% 0 200 400 600 800 1000 1200 2000 2005 2010 2015 KDD year Numberofsubmissions
  5. 5. KDD Reviewing Process 46 Senior PC members + 340 PC members • 2971 reviews in total (Rough) Acceptance rule: • Raw review score AND Standardized review score AND Raw meta-review AND Standardized meta-review score ≥ Weak Accept • 110 papers matched (immediate accepts) • Remaining papers were discussed with meta-reviewers and final decisions were made
  6. 6. Submissions per Country
  7. 7. Acceptance Rate per Country
  8. 8. Acceptance by Subject Area
  9. 9. Predicting Paper Acceptance Features Used Accuracy Random Guessing 0.50 Paper Abstract 0.57 Author Status (Past paper counts) 0.64 Author Status (DBLP graph connectivity) 0.61 Author Status (Counts + Graph) 0.65 Reviewer (Similarity, Graph distance to authors) 0.60 All (Abstract, Author Status, and Reviewer) 0.65
  10. 10. Predicting Paper Acceptance from the Review Text Features Used Paper: Accepted? Review: Score > 0? Random Guessing 0.50 0.50 Review Text 0.68 0.72 Review Text + Numeric Score (Novelty, Presentation) 0.77 0.77 Human Reading of Review Text 0.88 0.73
  11. 11. I’m submitting a paper: What correlates with acceptance?
  12. 12. Academia + Industry Papers do Better
  13. 13. Submissions per Author: 5 is best!
  14. 14. No benefit in submitting >5 papers!
  15. 15. Having more authors (seems to) help
  16. 16. It is the most experienced author that matters!
  17. 17. What insights can we gain on the review process?
  18. 18. Most reviews are Weak Rejects
  19. 19. More granularity is needed at the Weak Reject / Weak Accept level Reviewagreeswiththefinaloutcome
  20. 20. Review length is a good determinant of a review’s influence/quality Reviewagreeswiththefinaloutcome
  21. 21. Shorter reviews are used for clear accepts and rejects
  22. 22. Never review co-author’s papers
  23. 23. The Curse of the Review Submission Deadline
  24. 24. Over 50% reviews submitted in the last 5 days Over 20% reviews submitted in the last 24 hours 10% of reviews submitted late
  25. 25. Ratings increase near the deadline Weak Rejects increase while Rejects decrease
  26. 26. Reviews submitted late are less likely to agree with final outcome
  27. 27. Late reviews are shorter
  28. 28. Review quality drops: Accuracy of predicting score from review text
  29. 29. Conclusions • To get your papers accepted to KDD: – Collaborate in multidisciplinary teams – Have a senior author on board – Do not submit more than 5 papers • To improve KDD community standards: – Avoid Weak Reject/Weak Accept scores – Write longer and clearer reviews – Submit reviews early!

×