Many machine learning models we use today have the core assumption that our data needs to be tabular, but how often is this truly the case? What if our data points are not independent? By ignoring the potential interrelatedness of our data, do we lose meaningful information that our models cannot leverage? In this talk, we shall explore graph neural networks and highlight how they can solve interesting problems in a way that is intractable when limiting ourselves to using tabular data. We will look at the limitations of common algorithms and highlight how some clever linear algebra enables us to incorporate more meaningful information into our models. Social network data is a popular example of where relationships are relevant but relationships exist in many types of data where it may not be so obvious. Whether it's e-commerce, logistics or molecular data, relationships within your data likely exist and making use of them can be incredibly powerful. This talk will hopefully spark your curiosity and provide you with a way of looking at problems from a new angle. It is intended for anyone with an interest in machine learning and will only lightly touch on some technical details.

1. Using Graph Neural Networks To Embrace
The Dependency In Your Data
Usman Zafar
GoDataDriven

2. Introduction
l Machine Learning Engineer @ GoDataDriven
l Background in data science
l Fascinated by graphs and their problem solving abilities
2

3. Agenda
3
l Example use-case
l Common methods and their limitations
l How graphs can save the day
l Open-source tools to get started

4. Graphs are cool
4

5. Problem solving
5

6. Example use-case
6
l Predicting/categorising demand in new train
stations

7. Framing the problem
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8. Framing the problem
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9. Framing the problem
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10. Framing the problem
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11. Framing the problem
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12. Framing the problem
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13. Framing the problem...using a graph
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14. Encoding information in the edges
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15. Encoding information in the nodes
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16. Graph Labels
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17. Types of Problems
17
Node
Prediction

18. Types of Problems
18
Link
Prediction

19. Types of Problems
19
Graph
classification

20. Types of Problems
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Graph
classification

21. Node Prediction
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22. What Now?
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23. Adjacency Matrix
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A

24. Diagonal Degree Matrix
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D

25. Laplacian
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L = D - A

26. Node Features
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V

27. Edge Features
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E

28. ‘Local’ Context
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29. ‘Local’ Context
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30. ‘Local’ Context
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31. Message Passing
31

32. Message Passing
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33. The Mathematics
33
Polynomials of
Laplacian

34. The Mathematics
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Convolving a
feature vector

35. The Mathematics
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36. The Mathematics
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37. The Mathematics
37

38. The Mathematics
38

39. The Mathematics
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40. The Mathematics
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41. End-To-End Process
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42. End-To-End Process
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43. Performance
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[1] Cui et al, Traffic Graph Convolutional Recurrent Neural Network: A Deep Learning Framework for Network-Scale Traffic Learning and Forecasting, 2018

44. Graph Database
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Traditional SQL Query
Graph
Query

45. Spark GraphX
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46. PyTorch Geometric (Temporal)
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47. Q&A
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48. Thank you!
GoDataDriven