Graph and Tensor Mining: CMU SCS Insights

Discover the fascinating world of graph and tensor mining as explored by Luna Dong, Christos Faloutsos, Andrey Kan, Jun Ma, and Subho Mukherjee in the CMU SCS research. Delve into topics such as graph structures, tensors, embeddings, and inference methods showcased through visually appealing graphs and detailed content. Gain valuable insights from KDD 2018 presentations and explore cutting-edge techniques in a visually engaging format.

• Graph Mining
• Tensor Mining
• CMU SCS
• KDD 2018
• Inference Methods

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Presentation Transcript

1. CMU SCS Graph and Tensor Mining for fun and profit Luna Dong, Christos Faloutsos Andrey Kan, Jun Ma, Subho Mukherjee product graph

2. CMU SCS Roadmap Introduction Motivation Part#1: Graphs Part#2: Tensors P2.1: Basics (dfn, PARAFAC) P2.2: Embeddings & mining P2.3: Inference Conclusions KDD 2018 Dong+ product 2 graph

3. CMU SCS Recipe Structure: Problem definition Short answer/solution LONG answer details Conclusion/short-answer KDD 2018 Dong+ product 3 graph

4. CMU SCS Problem Definition Given existing triples Q: Is a given triple correct? KDD 2018 Dong+ product 4 graph

5. CMU SCS Short Answer Infer from other connecting paths Path 2 Path 1 Prec 1 Prec 0.03 Rec 0.01 Rec 0.33 F1 0.03 F1 0.04 Weight 2.62 Weight 2.19 KDD 2018 Dong+ product 5 graph

6. CMU SCS Roadmap Part#2: Tensors P2.1: Basics (dfn, PARAFAC) P2.2: Embeddings & mining P2.3: Inference Edge-based inference Path-based inference Conclusions KDD 2018 Dong+ product 6 graph

7. CMU SCS Edge-Based Inference Universal schema [Riedel et al., NAACL 13] historian-at professor-at (0.95) professor-at historian-at (0.05) Matrix factorization KDD 2018 Dong+ product 7 graph

8. CMU SCS Edge-Based Inference Feature Model (F): Entity Model (E): [Toutanova et al., EMNLP 15] KDD 2018 Dong+ product 8 graph

9. CMU SCS Edge-Based Inference Infer relation from a set of observed relations [Verga et al., ACL 16] KDD 2018 Dong+ product 9 graph

10. CMU SCS Roadmap Part#2: Tensors P2.1: Basics (dfn, PARAFAC) P2.2: Embeddings & mining P2.3: Inference Edge-based inference Path-based inference Conclusions KDD 2018 Dong+ product 10 graph

11. CMU SCS Path-Based Inference Path Ranking Algorithm (PRA) [Lao et al., EMNLP 11] Path 2 Prec 0.03 Path 1 Prec 1 Rec 0.33 Rec 0.01 F1 0.04 F1 0.03 Weight 2.19 Weight 2.62 KDD 2018 Dong+ product 11 graph

12. CMU SCS Path-Based Inference: Rule Mining Path Ranking Algorithm (PRA) [Lao et al., EMNLP 11] Features: paths Model: logistic regression KDD 2018 Dong+ product 12 graph

13. CMU SCS Path-Based Inference: Rule Mining Path Ranking Algorithm (PRA) [Lao et al., EMNLP 11] Features: paths Model: logistic regression More rule-mining approaches see afternoon tutorial: Fact checking: Theory and Practices KDD 2018 Dong+ product 13 graph

14. CMU SCS Revisit: Relation Embedding S1. What is the relationship among sub (h), pred (r), and obj (t)? Addition: h + r =?= t Multiplication: h r =?= t KDD 2018 Dong+ product 14 graph

15. CMU SCS Path-Based Inference: Embedding PathRNN: RNN to capture path [Neelakantan et al., ACL 15][Das et al., EMNLP 11] KDD 2018 Dong+ product 15 graph

16. CMU SCS Path-Based Inference: Embedding PathRNN: RNN to capture path [Neelakantan et al., ACL 15][Das et al., EMNLP 11] RNN KDD 2018 Dong+ product 16 graph

17. CMU SCS Path-Based Inference: Embedding PathRNN: RNN to capture path [Neelakantan et al., ACL 15][Das et al., EMNLP 11] Learned both seen paths and unseen paths KDD 2018 Dong+ product 17 graph

18. CMU SCS Conclusion/Short answer Infer from other connecting paths Path 2 Path 1 Prec 1 Prec 0.03 Rec 0.01 Rec 0.33 F1 0.03 F1 0.04 Weight 2.62 Weight 2.19 KDD 2018 Dong+ product 18 graph

19. CMU SCS Conclusion/Short answer S1. Edge-based inference (a.k.a., universal schema) Matrix factorization Embedding aggregation S2. Path-based inference Rule mining; e.g., PRA Embedding composition; e.g., PathRNN KDD 2018 Dong+ product 19 graph