Graph Deep Learning

We live in a networked world of complex relationships. Graph structured data captures these relationships (edges) between entities (nodes), as well as their associated properties. Think about your social network, a supply chain network, or even a Wikipedia knowledge graph. Each contains various patterns, interdependencies, and insights that can be revealed with proper context. Deep learning on graphs has lagged other segments of AI because the combinatorial complexity and nonlinearity of graphs requires long training times. Recently IBM Research and others have made big steps forward on scalability, triggering an exciting acceleration in the field. Graph learning is powerful for industry applications. A financial institution can analyze a transaction network to detect sophisticated money-laundering activity. A biotech firm can analyze protein- expression networks to understand the molecular basis of a disease. A manufacturing giant can analyze its massive supply chain to optimize inventory management and monitor risk. In each case, the fundamental algorithmic work must be done first – and we’re on it.

Top Work

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics

All Work

Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics
 
Graph Convolutional Networks for Temporal Action Localization
Graph Convolutional Networks for Temporal Action Localization
 
Online AI planning with graph neural networks and adaptive scheduling
Online AI planning with graph neural networks and adaptive scheduling
 
Reading between the lines with graph deep learning for NLP
Reading between the lines with graph deep learning for NLP
 
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
EvolveGCN: Evolving Graph Convolutional Networks for Dynamic Graphs
 
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
Imposing Label-Relational Inductive Bias for Extremely Fine-Grained Entity Typing
 
Scalable Graph Learning for Anti-Money Laundering: A First Look
Scalable Graph Learning for Anti-Money Laundering: A First Look
 
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders
Constrained Generation of Semantically Valid Graphs via Regularizing Variational Autoencoders