graph powered machine learning - iteblog.com
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@joerg_schad #Graph_ML
Graph Powered Machine Learning
Graph ML is the future of ML
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Gartner Top 10 Data and Analytics Trends for 2021In 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
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?
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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?
What is Graph ML?
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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?
What is Graph ML?
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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?
What problems can we solve?
Graph Analytics
Answer questions from Graph
- Community Detection
- Recommendations- Centrality- Path Finding- Fraud Detection- Permission
Management- ...
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Graph Embeddings and Graph Neural Networks
Learning Graphs- Node/Link Classification- Link Prediction- Classification of Graphs- ...
ML vs Graph ML Default ML assumption
Independent and identically distributed data
Graphs
Homophily - neighbours are similar
Structural Equivalent nodes
Heterophily - Neighbours are different
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Graph Database to Graph ML
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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
Different optionsCora 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)
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Graph ML: Even more Options Cora Citation Dataset Task: Predict Paper Label
- Label Propagation- Graph Convolutional Network- Embeddings and trad. ML
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Collaborative Filtering
https://blog.dgraph.io/post/recommendation/https://grouplens.org/datasets/movielens/100k/
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What should I watch next...
https://www.independent.co.uk/arts-entertainment/films/features/films-best-watch-coronavirus-isolation-quarantine-movies-classic-greatest-essential-list-a9394006.html
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User MovieRates
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User MovieRates
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 stars2. Find users who also rated these
movies also with 5 stars3. Find additional movies also
rated 5 stars by those users
Agenda ML Infrastructure & Metadata
Graphs
Graph Database
Graph Analytics
Graph Embeddings I
Graphs Neural Networks
What are Embeddings?
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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
Node2Vec
19https://arxiv.org/pdf/1607.00653.pdfhttps://towardsdatascience.com/graph-embeddings-the-summary-cc6075aba007
Agenda ML Infrastructure & Metadata
Graphs
Graph Database
Graph Analytics
Graph Embeddings I
Graphs Neural Networks
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
23https://dsgiitr.com/blogs/gat/https://arxiv.org/pdf/1710.10903.pdf
Deep Walk vs Node2Vec vs GCN vs Graph Sage
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Deep Walk Node2Vec GCN Graph Sage GAT
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
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