Near-Optimal Fully Dynamic Densest Subgraph

Near-Optimal Fully Dynamic Densest Subgraph

Association for Computing Machinery (ACM) via YouTube Direct link

We want approximate: allow some slack

16 of 22

16 of 22

We want approximate: allow some slack

Class Central Classrooms beta

YouTube videos curated by Class Central.

Classroom Contents

Near-Optimal Fully Dynamic Densest Subgraph

Automatically move to the next video in the Classroom when playback concludes

  1. 1 Intro
  2. 2 Overview
  3. 3 Dense subgraphs
  4. 4 Motivation - correlation mining
  5. 5 Motivation - fraud detection
  6. 6 Motivation - story identification
  7. 7 Definition of density
  8. 8 Algorithms for static densest subgraph
  9. 9 Why dynamic algorithms
  10. 10 Our goal - fully dynamic algorithm for Densest Subgraph
  11. 11 Algorithms for dynamic densest subgraph
  12. 12 LP formulation
  13. 13 Dual of the LP
  14. 14 Dual LP: load balancing visualization
  15. 15 Dual LP: local optimality
  16. 16 We want approximate: allow some slack
  17. 17 Visualize as graph orientation problem
  18. 18 Dynamic graph orientation
  19. 19 Bounding number of flips
  20. 20 Dealing with dynamic
  21. 21 Runtime
  22. 22 Recap

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Someone learning on their laptop while sitting on the floor.