1 / 18

Large Graph Mining: Power Tools and a Practitioner’s guide

Large Graph Mining: Power Tools and a Practitioner’s guide. Task 4: Center-piece Subgraphs Faloutsos, Miller and Tsourakakis CMU. Outline. Introduction – Motivation Task 1: Node importance Task 2: Community detection Task 3: Recommendations Task 4: Connection sub-graphs

Download Presentation

Large Graph Mining: Power Tools and a Practitioner’s guide

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Large Graph Mining:Power Tools and a Practitioner’s guide Task 4: Center-piece Subgraphs Faloutsos, Miller and Tsourakakis CMU Faloutsos, Miller, Tsourakakis

  2. Outline Introduction – Motivation Task 1: Node importance Task 2: Community detection Task 3: Recommendations Task 4: Connection sub-graphs Task 5: Mining graphs over time … Conclusions Faloutsos, Miller, Tsourakakis

  3. Detailed outline • Problem definition • Solution • Results H. Tong & C. Faloutsos Center-piece subgraphs: problem definition and fast solutions. In KDD, 404-413, 2006. Faloutsos, Miller, Tsourakakis

  4. Center-Piece Subgraph(Ceps) • Given Q query nodes • Find Center-piece ( ) • Input of Ceps • Q Query nodes • Budget b • k softAnd number • App. • Social Network • Law Inforcement • Gene Network • … Faloutsos, Miller, Tsourakakis

  5. Challenges in Ceps • Q1: How to measure importance? • (Q2: How to extract connection subgraph? • Q3: How to do it efficiently?) Faloutsos, Miller, Tsourakakis

  6. Challenges in Ceps • Q1: How to measure importance? • A: “proximity” – but how to combine scores? • (Q2: How to extract connection subgraph? • Q3: How to do it efficiently?) Faloutsos, Miller, Tsourakakis

  7. AND: Combine Scores • Q: How to combine scores? Faloutsos, Miller, Tsourakakis

  8. AND: Combine Scores • Q: How to combine scores? • A: Multiply • …= prob. 3 random particles coincide on node j Faloutsos, Miller, Tsourakakis

  9. K_SoftAnd: Relaxation of AND What if AND query No Answer? Disconnected Communities Noise Faloutsos, Miller, Tsourakakis

  10. K_SoftAnd: Combine Scores Generalization – SoftAND: We want nodes close to k of Q (k<Q) query nodes. Q: How to do that? Faloutsos, Miller, Tsourakakis

  11. K_SoftAnd: Combine Scores Generalization – softAND: We want nodes close to k of Q (k<Q) query nodes. Q: How to do that? A: Prob(at least k-out-of-Q will meet each other at j) Faloutsos, Miller, Tsourakakis

  12. AND query vs. K_SoftAnd query And Query x 1e-4 2_SoftAnd Query Faloutsos, Miller, Tsourakakis

  13. 1_SoftAnd query = OR query Faloutsos, Miller, Tsourakakis

  14. Detailed outline • Problem definition • Solution • Results Faloutsos, Miller, Tsourakakis

  15. Case Study: AND query Faloutsos, Miller, Tsourakakis

  16. Case Study: AND query Faloutsos, Miller, Tsourakakis

  17. database Statistic Faloutsos, Miller, Tsourakakis 2_SoftAnd query

  18. Conclusions Proximity (e.g., w/ RWR) helps answer ‘AND’ and ‘k_softAnd’ queries Faloutsos, Miller, Tsourakakis

More Related