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Will Nathan like Camille? Will Nathan vote for candidate T.? 2 Will Nathan like Camille? Will Nathan vote for candidate T.? 3 4 5 word2vec can be


  1. × Will Nathan like Camille? Will Nathan vote for candidate T.? • • • • • • 2

  2. ↔ Will Nathan like Camille? Will Nathan vote for candidate T.? • • • • 3

  3. 4

  4. ≈ → 𝑋 𝑈 𝑋 5

  5. • • word2vec can be understood as matrix factorization! dimensionality = number of nodes 6

  6. Random walks converge to Personalized PageRank 𝑡𝑗𝑛(𝑤,⋅) 𝑡𝑗𝑛(𝑣,⋅) 𝑣 𝑤 𝑣 𝑤 7

  7. 𝑣 𝑣 𝑋 𝑈 𝑋 8

  8. • Why should I bother • about similarities? • • • • 9

  9. 𝑡𝑗𝑛(𝑣,⋅) 𝑋~𝒪(0, 1) 𝑣 ∈ 𝑊 𝑋 𝑡𝑗𝑛(𝑣,⋅) 𝑃 𝑜 2 𝑡𝑗𝑛(𝑣,⋅) 𝑣 𝑋 𝑈 𝑋 We make it faster with sampling! 10

  10. Why not just using Negative Sampling? 11

  11. × • • • • • • 12

  12. × 13

  13. × 14

  14. × 15

  15. 16

  16. Thank you for your attention! Thank you for attention! bit.ly/www-verse github.com/xgfs/verse Anton Tsitsulin Knowledge Discovery and Data Mining Hasso Plattner Institute

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