Mining Interesting Meta-Paths from Complex Heterogeneous Information Networks
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This document discusses mining interesting meta-paths from complex heterogeneous information networks. It begins by introducing homogeneous and heterogeneous networks and some example meta-paths. It then discusses the limitations of current meta-path related research approaches. The document proposes a framework that can handle large-scale heterogeneous networks with complex meta-type hierarchies and automatically generate meta-paths. It describes generating paths between objects and ranking paths based on meta-data to extract interesting meta-paths.
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Mining Interesting Meta-Paths from Complex Heterogeneous Information Networks
- 1. Mining Interesting Meta-Paths from Complex Heterogeneous Information Networks Baoxu Shi, Tim Weninger University of Notre Dame 1
- 3. Heterogeneous Network Association People University City Country Conference Workshop Belongs to Speaks at locates at locates at the capital of affiliate Professor of 3 MoDAT
- 4. Heterogeneous Network Belongs to Speaks at locates at locates at the capital of affiliate Professor at People Association Meeting Education Geography Meeting Geography
- 6. Path and Meta-Path PeopleMeeting Education Geography Association
- 7. How things are uniquely connected/separated? NORTHEASTERN BARABÁSI NOTRE DAME SOUTH BEND 3 EDUCATION GEOGRAPHYPEOPLE Path Meta-Path 1 2 1 2 3 EDUCATION Interesting meta-path is meta-path that best describes how two objects are uniquely related in complex HINs. 7
- 8. NORTHEASTERN BARABÁSI NOTRE DAME SOUTH BEND 3 EDUCATION GEOGRAPHYPEOPLE Path Meta-Path 1 2 1 2 3 EDUCATION Education Professor University Geography
- 9. NORTHEASTERN BARABÁSI NOTRE DAME SOUTH BEND 3 EDUCATION GEOGRAPHYPEOPLE Path Meta-Path 1 2 1 2 3 EDUCATION Education Professor University Geography Education Network Scientist Catholic University Geography 9
- 10. NORTHEASTERN BARABÁSI NOTRE DAME SOUTH BEND 3 EDUCATION GEOGRAPHYPEOPLE Path Meta-Path 1 2 1 2 3 EDUCATION Education Professor University Geography Education Network Scientist Catholic University Geography Education Network Scientist who born in Transylvania,1967 Catholic University at South Bend, IN Geography 10
- 11. Limitations of State of the Art Meta-Path Related Researches • Type of meta-labels are limited • Meta-types do not have complex hierarchy • Meta-paths are pre-defined manually • No large scale experiments Term Venue Paper Author 11 NORTHEASTERN BARABÁSI NOTRE DAME SOUTH BEND 3 EDUCATION GEOGRAPHYPEOPLE Path Meta-Path 1 2 1 2 3 EDUCATION
- 12. Limitations of State of the Art Meta-Path Related Researches • Type of meta-labels are limited • Meta-types do not have complex hierarchy • Meta-paths are pre-defined manually • No large scale experiments Framework that can handle millions of meta-types Meta-types with complex hierarchy Meta-path are automatically generated Experiments are done on Wikipedia (10 million nodes, 740 million edges) 12
- 13. How to find interesting paths? • Generate paths • Rank top k interesting paths using meta-data • Extract meta-path for searching 13
- 14. Path Generation sib(ai, aj) i↵ ai 2 t ^ aj 2 t 8au0 2 A0 u, sib(au0 , au) 8av0 2 A0 v, sib(av0 , av) {~y1, ~y2, . . .} 2 Y ~y = ha1, a2, . . . , a|~y|ii ~x = ha1, a2, . . . , a|~x|ii{~x1, ~x2, . . . , ~xk} 2 X au = a1, av = a|~x|, 1 i k • Generate path set for given points • Generate sibling path set 14 a1 2 A0 u, a|~y| 2 A0 v, 1 i k au, av
- 15. ROCKNE WAND AMERICAN COMPUTER SCIENTISTS PROGRAMMING LANGUAGE RESEARCHERS 983 Others BARBARA LISKOV ANDERS_HEJLSBERG 79 Others UNIVERSITY OF NOTRE DAME FACULTY COLLEGE FOOTBALL HALL OF FAME INDUCTEES 21 Others JOHN HEISMAN BARRY SANDERS JULIUS NIEUWLAND BARABÁSI 204 Others 1075 OthersHAL ABELSON Y X VASANT HONAVAR Short Paths Example: Path generation 15
- 16. ROCKNE NORTHEASTERN NOTRE DAME WAND BARABÁSI ROSE BOWLHARVARD CY YOUNG CARL HUBBELL CARNEGIE MELLON UNIVERSITY TD GARDEN LA COLISEUM Example: Path generation 16 Which is the most interesting path?
- 17. Path Ranking • Unordered Ranking ~xi = ha1, a2, . . . , a|~xi|i ~T~xi = hTa1 , Ta2 , . . . , Ta|~xi| i T~xi = |~xn| [ n=1 Tan TY = |Y | [ i=1 {T~yi }T~yi = Ta0 u [ |~yi| 1 [ n=2 {Tan } [ Ta0 v r(~xi) = |T~xi TY | |TY |
- 18. Path Ranking • Ordered Ranking ~xi = ha1, a2, . . . , a|~xi|i ~T~xi = hTa1 , Ta2 , . . . , Ta|~xi| i p(an, a0 n) = |Tan TYn | |TYn | r(~xi) = mean |~xi| n=1(p(an, a0 n))
- 19. 19 Result: Path Ranking Qualitative analysis is done with mechanical turkers.
- 20. 20 ● ● ● ● ● 0.48 0.52 0.56 0.60 0 0.25 0.5 0.75 1 Result: Path Ranking Result shows user more like to pick path with lowest or highest similarity. People pick path with highest score may because they treat best as correct.
- 21. DATA MINERS JIAWEI HAN DATA MINING SIGKDD JOHANNES GEHRKE STATISTICIANS MATHEMATICIANS PEOPLE SCHOLARS AND ACADEMICS DATA MINING SCIENCE ACM SIGS PEOPLE MorespecificMoregeneral Nodes Types COMPUTATIONAL STATISTICS MATHEMATICAL SCIENCES STATISTICS SOCIETY ACM PROFESSIONAL ORGANIZATIONS SCIENTIFIC SOCIETIES DATABASE RESEARCHERS COMPUTER SCIENTISTS SCHOLARS AND ACADEMICS SCHOLARS ORGANIZATIONS Example: Extract Meta-Path
- 22. 22 Result: Meta-Path Constraint RWR 0 0.24 0.41 0.48 Edgar F. Codd 40.5 18.1 9.0 Johannes Gehrke 28.4 29.4 8.4 2.8 Raghu Ramakrishnan 31.1 6.0 3.6 Anita Borg 5.1 0.6 0.2 Shafi Goldwasser 4.9 0.6 Osmar R. Zaiane 4.8 3.6 1.6 Vint Cerf 4.1 2.4 0.2 Allen Newell 2.0 0.6 ACM 5.1 IEEE 4.9 Yahoo! Research 4.8 Microsoft Research 4.4 Database researchers Computer Scientist
- 23. Questions?