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Graph-Powered Machine Learning
The document discusses the significance of graph machine learning (Graph ML) in providing insights and predictions based on relationships within data, as emphasized by Dr. James Fowler. It outlines various applications of Graph ML, including community detection, recommendations, and fraud detection, alongside comparisons with traditional machine learning. Additionally, it highlights techniques like graph embeddings and neural networks as essential components in processing and analyzing graph data.
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Graph-Powered Machine Learning
- 1. @joerg_schad #Graph_ML Graph PoweredMachine Learning
- 2. Graph ML isthe 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. Jörg Schad, PhD ●Previous ○ Suki.ai ○ Mesosphere ○ PhD Distributed DB Systems ● @joerg_schad @joerg_schad
- 4. Agenda ML Infrastructure& Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
- 5. What is GraphML? 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. What is GraphML? 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. What is GraphML? 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. What problems canwe 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. ML vs GraphML Default ML assumption Independent and identically distributed data Graphs Homophily - neighbours are similar Structural Equivalent nodes Heterophily - Neighbours are different 9
- 10. Graph Database toGraph 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. Different options Cora CitationDataset 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. Graph ML: Evenmore Options Cora Citation Dataset Task: Predict Paper Label - Label Propagation - Graph Convolutional Network - Embeddings and trad. ML 12
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- 14. 14 What should Iwatch 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
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- 16. 16 User Movie Rates How tofind 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. Agenda ML Infrastructure& Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
- 18. What are Embeddings? 18 Wordembedding 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
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- 20. Agenda ML Infrastructure& Metadata Graphs Graph Database Graph Analytics Graph Embeddings I Graphs Neural Networks
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- 22. http://snap.stanford.edu/graphsage/ https://cs.stanford.edu/people/jure/pubs/graphsage-nips17.pdf 22 Two problemswith early GNNs - assume fixed graph size/structure - Unknown graphs - Changing structure - Consider entire graph during training - Scalability - ≠ mini-batches in NN
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- 24. Deep Walk vsNode2Vec vs GCN vs Graph Sage 24 Deep Walk Node2Vec GCN Graph Sage GAT
- 25. Thanks for listening! Whereto 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. Feedback Your feedback isimportant to us. Don’t forget to rate and review the sessions.