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@joerg_schad #Graph_ML
Graph Powered Machine Learning
Graph ML is the future of ML
2
Gartner Top 10 Data and Analytics Trends for 2021
In fact, as many as 50% of Gartner client...
Jörg Schad, PhD
● Previous
○ Suki.ai
○ Mesosphere
○ PhD Distributed
DB Systems
● @joerg_schad
@joerg_schad
Agenda ML Infrastructure &
Metadata
Graphs
Graph Database
Graph Analytics
Graph Embeddings I
Graphs Neural Networks
What is Graph ML?
5
Graph Query
‣ Who can introduce me to x?
Graph Analytics
‣ Who is the most connected persons?
Graph ML...
What is Graph ML?
6
Graph Query
‣ Who can introduce me to x?
Graph Analytics
‣ Who is the most connected persons?
Graph ML...
What is Graph ML?
7
Graph Query
‣ Who can introduce me to x?
Graph Analytics
‣ Who is the most connected persons?
Graph ML...
What problems can we solve?
Graph Analytics
Answer questions from
Graph
- Community
Detection
- Recommendations
- Centrali...
ML vs Graph ML
Default ML assumption
Independent and identically distributed data
Graphs
Homophily - neighbours are simila...
Graph Database to Graph ML
10
Graph Queries
Identify an explicit
pattern
E.g., Find common
connections
between two
people ...
Different options
Cora Citation Dataset Graph Query
- Who cited paper x?
Graph Algorithm/Analytics
- Most cited paper
- Su...
Graph ML: Even more Options
Cora Citation Dataset Task: Predict Paper Label
- Label Propagation
- Graph Convolutional Netw...
13
Collaborative Filtering
https://blog.dgraph.io/post/recommendation/
https://grouplens.org/datasets/movielens/100k/
14
What should I watch next...
https://www.independent.co.uk/arts-entertainment/films/features/films-best-wat
ch-coronavir...
15
User Movie
Rates
16
User Movie
Rates
How to find movies I like?
Collaborative Filtering
“Find highly rated movies, by people
who also like m...
Agenda ML Infrastructure &
Metadata
Graphs
Graph Database
Graph Analytics
Graph Embeddings I
Graphs Neural Networks
What are Embeddings?
18
Word embedding is the collective name for a set of language
modeling and feature learning techniqu...
Node2Vec
19
https://arxiv.org/pdf/1607.00653.pdf
https://towardsdatascience.com/graph-embeddings-the-summary-cc6075aba007
Agenda ML Infrastructure &
Metadata
Graphs
Graph Database
Graph Analytics
Graph Embeddings I
Graphs Neural Networks
21
https://www.youtube.com/watch?v=uF53xsT7mjc
http://snap.stanford.edu/graphsage/
https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf 22
Two problems with ear...
23
https://dsgiitr.com/blogs/gat/
https://arxiv.org/pdf/1710.10903.pdf
Deep Walk vs Node2Vec vs GCN vs Graph Sage
24
Deep Walk Node2Vec GCN Graph Sage GAT
Thanks for listening!
Where to go next?
- Stanford CS224W: Machine Learning with Graphs (+ videos)
- Tomas Kipf Blog
- Gra...
Feedback
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Graph-Powered Machine Learning

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Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.

Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.

Graph-Powered Machine Learning

  1. 1. @joerg_schad #Graph_ML Graph Powered Machine Learning
  2. 2. Graph ML is the future of ML 2 Gartner Top 10 Data and Analytics Trends for 2021 In fact, as many as 50% of Gartner client inquiries around the topic of AI involve a discussion around the use of graph technology. “You can make better predictions utilizing relationships within the data than you can from just the data alone.” — Dr. James Fowler (UC San Diego) DeepMind ETA Prediction
  3. 3. Jörg Schad, PhD ● Previous ○ Suki.ai ○ Mesosphere ○ PhD Distributed DB Systems ● @joerg_schad @joerg_schad
  4. 4. Agenda ML Infrastructure & Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
  5. 5. What is Graph ML? 5 Graph Query ‣ Who can introduce me to x? Graph Analytics ‣ Who is the most connected persons? Graph ML  ‣ Predict potential connections? ‣ Who is likely to churn?
  6. 6. What is Graph ML? 6 Graph Query ‣ Who can introduce me to x? Graph Analytics ‣ Who is the most connected persons? Graph ML  ‣ Predict potential connections? ‣ Who is likely to churn?
  7. 7. What is Graph ML? 7 Graph Query ‣ Who can introduce me to x? Graph Analytics ‣ Who is the most connected persons? Graph ML  ‣ Predict potential connections? ‣ Who is likely to churn?
  8. 8. What problems can we solve? Graph Analytics Answer questions from Graph - Community Detection - Recommendations - Centrality - Path Finding - Fraud Detection - Permission Management - ... 8 Graph Embeddings and Graph Neural Networks Learning Graphs - Node/Link Classification - Link Prediction - Classification of Graphs - ...
  9. 9. ML vs Graph ML Default ML assumption Independent and identically distributed data Graphs Homophily - neighbours are similar Structural Equivalent nodes Heterophily - Neighbours are different 9
  10. 10. Graph Database to Graph ML 10 Graph Queries Identify an explicit pattern E.g., Find common connections between two people at LinkedIn Graph Algorithms Function beyond select/filter E.g., Find shortest path between two cities Graph Analytics Get insight from Graphs E.g., Identify subcommunities in my Graph Graph ML Train ML Models based on Graphs E.g., Graph Convolutional Networks or Graph Embeddings as input to TensorFlow
  11. 11. Different options Cora Citation Dataset Graph Query - Who cited paper x? Graph Algorithm/Analytics - Most cited paper - Sub Communities Graph ML - Predict reviewers - Predict missing citations - Predict paper labels (or other features) 11
  12. 12. Graph ML: Even more Options Cora Citation Dataset Task: Predict Paper Label - Label Propagation - Graph Convolutional Network - Embeddings and trad. ML 12
  13. 13. 13 Collaborative Filtering https://blog.dgraph.io/post/recommendation/ https://grouplens.org/datasets/movielens/100k/
  14. 14. 14 What should I watch next... https://www.independent.co.uk/arts-entertainment/films/features/films-best-wat ch-coronavirus-isolation-quarantine-movies-classic-greatest-essential-list-a939 4006.html
  15. 15. 15 User Movie Rates
  16. 16. 16 User Movie Rates How to find movies I like? Collaborative Filtering “Find highly rated movies, by people who also like movies I rated highly” 1. Find movies I rated with 5 stars 2. Find users who also rated these movies also with 5 stars 3. Find additional movies also rated 5 stars by those users
  17. 17. Agenda ML Infrastructure & Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
  18. 18. What are Embeddings? 18 Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words or phrases from the vocabulary are mapped to vectors of real numbers. Wikipedia https://towardsdatascience.com/creating-word-embeddings-coding-the-word2vec-algorithm-in-python-using-deep-learning-b337d0ba17a8
  19. 19. Node2Vec 19 https://arxiv.org/pdf/1607.00653.pdf https://towardsdatascience.com/graph-embeddings-the-summary-cc6075aba007
  20. 20. Agenda ML Infrastructure & Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
  21. 21. 21 https://www.youtube.com/watch?v=uF53xsT7mjc
  22. 22. http://snap.stanford.edu/graphsage/ https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf 22 Two problems with early GNNs - assume fixed graph size/structure - Unknown graphs - Changing structure - Consider entire graph during training - Scalability - ≠ mini-batches in NN
  23. 23. 23 https://dsgiitr.com/blogs/gat/ https://arxiv.org/pdf/1710.10903.pdf
  24. 24. Deep Walk vs Node2Vec vs GCN vs Graph Sage 24 Deep Walk Node2Vec GCN Graph Sage GAT
  25. 25. Thanks for listening! Where to go next? - Stanford CS224W: Machine Learning with Graphs (+ videos) - Tomas Kipf Blog - Graph Representation Learning Repository - Graph Representation Learning Book - https://towardsdatascience.com/graph-deep-learning/home
  26. 26. Feedback Your feedback is important to us. Don’t forget to rate and review the sessions.
  • MuhammadSalar2

    Jul. 11, 2021

Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further. Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.

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