Shift AI 2020: Graph Deep Learning for Real-World Applications | Mark Weber (MIT-IBM Watson AI Lab)
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Shift AI was a success, connecting hundreds of professionals that were eager to propel the progress of AI and discuss the newest technologies in data mining, machine learning and neural networks. More at https://ai.shiftconf.co/. Talk description: Try to reason about something without any context. It’s possible, but your understanding will be limited and brittle. That’s because relationships between things give us critical information. In mathematics, we can model relational data as a graph or network structure -- nodes, edges, and the attributes associated with each. While deep learning has done remarkable things on Euclidean data (e.g. audio, images, video) graph deep learning has lagged because combinatorial complexity and nonlinearity issues making training very difficult and expensive. Yet it’s precisely the information hidden in that complexity that makes graphs so interesting. In this talk, Mark Weber will introduce a class of methods known as scalable graph convolutional networks (GCN) and share experimental results from a semi-supervised anomaly detection task in financial forensics and anti-money laundering. We will take a closer look at a new method developed at MIT-IBM called EvolveGCN, which uses recurrent neural network architectures (RNN) for handling temporal dynamism. We will discuss the implication of these results in anti-money laundering and beyond.
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Shift AI 2020: Graph Deep Learning for Real-World Applications | Mark Weber (MIT-IBM Watson AI Lab)
- 2. Graph Deep Learning Venue Date Mark Weber @markrweber #MITIBM Charles Leiserson Jie Chen Toyotaro Suzumura Shift AI Conference April 14, 2020 Graph Deep Learning Explainability Efficient AI
- 3. 9 Contents Graph Convolutional Networks Use Case: Anti-Money Laundering Evolutionary Dynamism 1 Graphs 2 GCN’s 1 The Elliptic Data Set 2 Experimental Results 1 EvolveGCN 2 Results 3 Next Steps
- 5. 11 Graph Convolutional Networks MIT-IBM Watson AI Lab Graph Deep Learning 2020 Source: Kipf & Welling
- 6. 12 Graph Convolutional Networks MIT-IBM Watson AI Lab Graph Deep Learning 2020 NeurIPS 2018
- 7. The existential question of node embedding. W T W T W T W T W T W T Who am I? H(l+1) = ( ˆAH(l) W(l) ) h(l+1) (v) = ✓Z ˆA(v, u)h(l) (u)W(l) dP(u) ◆
- 8. 14 Applications of Graph Learning Operations Supply chains, logistics, e.g. The traveling salesman problem in combinatorial optimization Molecular Structure Discovering new antibiotics and predicting antibiotic resistance Electronics Design NVIDIA processing irregular graph representations of logic circuits Finance Risk management, forecasting, anti-money laundering, and more MIT-IBM Watson AI Lab Graph Deep Learning 2020
- 9. 15 Anti-Money Laundering MIT-IBM Watson AI Lab Presentation template 2020 Image: Ozark on Netflix
- 10. 800,000 people are “exported” annually in a $40 billion human trafficking industry enslaving 40 million people
- 11. 37 153 529 Global Aid Remittances 2018 Cash Flows to Low-to-Middle-Income Countries (Billions USD) World Bank Report “The Financial Action Task Force, recognizing that overly cautious Anti-Money Laundering and Terrorist Financing (AML/CFT) safeguards can have the unintended consequence of excluding legitimate businesses and consumers from the financial system, has emphasized the need to ensure that such safeguards also support financial inclusion.”
- 12. 19 AML as a Graph Problem: Spotlight on the 1MDB Scheme MIT-IBM Watson AI Lab Graph Deep Learning 2020 Jho Low (right) and the 1Malaysia Development Berhad (1MDB) allegedly robbed the Malaysian people of over $11 billion in taxpayer funds earmarked for the nation's development. Ironically, part of the laundering scheme financed The Wolf of Wallstreet.
- 13. 20 AML as a Graph Problem: Spotlight on the 1MDB Scheme MIT-IBM Watson AI Lab Graph Deep Learning 2020
- 14. 21 Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics Published in the KDD Anomaly Detection in Finance Workshop, 2019 Presented to the U.S. Securities & Exchange Commission Insight: GCN node embeddings as an input feature boosts model accuracy and precision MIT-IBM Watson AI Lab Graph Deep Learning 2020
- 15. 22 203,769 nodes (Bitcoin transactions) 234,355 edges (directed flows) 21% licit labels (known exchanges, wallet providers, miners, licit services, etc.) 2% illicit labels (known scams, malware, terrorist organizations, ransomware, Ponzi schemes, etc.) 94 local features (LF) e.g. time step, in/out count activity, transaction fee 72 one-hop aggregate features (AF) (e.g. max, min, standard deviation, and correlation coefficients of the neighbor transactions) MIT-IBM Watson AI Lab Graph Deep Learning 2020 The Elliptic Data Set
- 17. 24 Task: imbalanced, binary classification targeting illicit transactions Hyperparameters: Trained GCN using weighted cross entropy loss to prioritize illicit nodes Inputs: Local Features (LF), Aggregate Features (AF), Node Embeddings (NE) Methods: Logistic Regression (LR), Multilayer Perceptron (MLP), Graph Convolutional Network (GCN), and Random Forest (RF) Note: important to integrate the real-world, human considerations MIT-IBM Watson AI Lab Graph Deep Learning 2020 Experiments Method Precision Recall F1 Accuracy Precision Recall Don’t exclude innocent people Quarantine no one Catch all bad guys Quarantine everyone
- 18. 25 How well does GCN extract relational information? Very well. Can we boost Precision without sacrificing Recall? Sometimes. This needs more study. Which model wins? The GCN-boosted Random Forest. MIT-IBM Watson AI Lab Graph Deep Learning 2020 Method Precision Recall F1 Accuracy LR LF 0.348 0.668 0.457 LR AF 0.404 0.593 0.481 LR AF+NE 0.537 0.528 0.533 MLP AF 0.694 0.617 0.653 MLP AF+NE 0.780 0.617 0.689 GCN 0.812 0.512 0.628 RF AF 0.956 0.670 0.788 RF AF+NE 0.971 0.675 0.796 Results
- 19. 26MIT-IBM Watson AI Lab Graph Deep Learning 2020
- 20. 27MIT-IBM Watson AI Lab Presentation template 2020
- 21. 28 EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs Published in the AAAI 2020 Patent-pending algorithm EvolveGCN. Code is open-source. Insight: a recurrent neural network (RNN) architecture allows GCN to capture relational system dynamics over time MIT-IBM Watson AI Lab Presentation template 2020
- 22. 29MIT-IBM Watson AI Lab Graph Deep Learning 2020 EvolveGCN: An RNN Architecture GCN 1 GCN 2 GCN 3 Layer 2 Weights Layer 1 Weights Layer 2 Weights Layer 1 Weights Layer 2 Weights Layer 1 Weights RNN 1 RNN 2 RNN 1 RNN 2 Node EmbeddingNode EmbeddingNode Embedding
- 23. 30MIT-IBM Watson AI Lab Graph Deep Learning 2020 Updating the Weight Matrix 1 EvolveGCN-H 2 EvolveGCN-O
- 24. 31 EvolveGCN Experiments MIT-IBM Watson AI Lab Graph Deep Learning 2020 Across seven data sets, EvolveGCN generally outperforms alternative dynamic algorithms Example: node and edge classification
- 25. 32 EvolveGCN Experiments MIT-IBM Watson AI Lab Graph Deep Learning 2020 But we still fail to endure the dark market collapse because the event has not been learned by the model Can future models learn Black Swans instead of omitting them in training?
- 26. 33 Contents Graph Convolutional Networks Use Case: Anti-Money Laundering Evolutionary Dynamism 1 Graphs 2 GCN’s 1 The Elliptic Data Set 2 Experimental Results 1 EvolveGCN 2 Results 3 Next Steps
- 27. Graph Deep Learning Event Date Mark Weber @markrweber #MITIBM Charles Leiserson Jie Chen Toyotaro Suzumura Quarantine April 14, 2020 Graph Deep Learning Explainability Efficient AI