CMU SCS Graph and Tensor Mining for fun

CMU SCS Graph and Tensor Mining for fun and profit Luna Dong, Christos Faloutsos Andrey Kan, Jun Ma, Subho Mukherjee Amazon - CMU

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs [break] • Part#2: Tensors • Conclusions KDD 2018 Dong+ 2

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ 3

CMU SCS Why study graphs? fb>$10 B; ~1 B users KDD 2018 Dong+ 4

CMU SCS Why study graphs? Internet Map [lumeta. com] Food Web [Martinez ’ 91] Protein Interactions [genomebiology. com] Friendship Network [Moody ’ 01] KDD 2018 Dong+ 5

CMU SCS e-commerce examples • Recommendation systems • . . … … KDD 2018 Dong+ 6

CMU SCS e-commerce examples Who-buys-what … … KDD 2018 Dong+ 7

CMU SCS e-commerce examples … Who-buys-what Who-sells-what … KDD 2018 Dong+ 8

CMU SCS e-commerce examples Dong+ … KDD 2018 … Who-buys-what Who-sells-what Who-reviews-what 9

CMU SCS More examples KDD 2018 Dong+ … … Who-buys-what Who-sells-what Who-reviews-what Who-queries-what Which_machine - connects_to - what … <subject> related-to <object> : graph 10

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs ? ? – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ 11

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ ? 12

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ 13

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ ? 14

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ ? 15

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ 16

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs [break] • Part#2: Tensors • Conclusions KDD 2018 Dong+ 17

CMU SCS Tensors, e. g. , time-evolving graphs • What is ‘normal’? suspicious? Groups? 3 am, 4/1 … 10 pm, 4/3 11 pm, 4/3 KDD 2018 Dong+ 18

CMU SCS Tensors, e. g. , Multi. View Graph • What is ‘normal’? suspicious? Groups? likes … buys … reviews buys KDD 2018 Dong+ 19

CMU SCS Tensors, e. g. , Knowledge Graph • What is ‘normal’? Forecast? Spot errors? directed … dates … Acted_in born_in … KDD 2018 produced Dong+ 20

CMU SCS ‘Recipe’ Structure: • Problem definition • Short answer/solution • LONG answer – details • Conclusion/short-answer KDD 2018 Dong+ 21

CMU SCS ‘Recipe’ Structure: • Problem definition • Short answer/solution • LONG answer – details • Conclusion/short-answer KDD 2018 Dong+ 22

CMU SCS Roadmap • Introduction – Motivation • Part#1: Graphs ? ? – P 1. 1: properties/patterns in graphs – P 1. 2: node importance – P 1. 3: community detection – P 1. 4: fraud/anomaly detection – P 1. 5: belief propagation KDD 2018 Dong+ 23