How AWS uses graph neural networks to meet customer needs

Information extraction, drug discovery, and software analysis are just a few applications of this versatile tool.

Graphs are an information-rich way to represent data. A graph consists of nodes — typically represented by circles — and edges — typically represented as line segments between nodes. In a knowledge graph, for instance, the nodes represent entities, and the edges represent relationships between them. In a social graph, the nodes represent people, and an edge indicates that two of those people know each other.

At Amazon Web Services, the use of machine learning (ML) to make the information encoded in graphs more useful to our customers has been a major research focus. In this post, we’ll showcase a variety of graph ML applications that customers have developed in collaboration with AWS scientists, from malicious-account detection and automated document processing to knowledge-graph-assisted drug discovery and protein property prediction.

Introduction to graph learning

Graphs can be homogenous, meaning the nodes represent a single type of entity (say, airports), and the edges represent a single type of relationship (say, scheduled flights). Or they can be heterogeneous, meaning they integrate multiple types of relationships among different entities, such as a graph of customers and products connected by both purchase histories and interests, or a knowledge graph of drugs, diseases, genes, and biological pathways connected by relationships such as indication and regulation. Nodes are often associated with data features, such as a product’s price or text description.

Heterogeneous knowledge graph
In a heterogenous knowledge graph, nodes can represent different classes of objects.

Graph neural networks

In the past 10 years, deep learning has revolutionized a host of AI applications, from natural-language processing to speech synthesis to computer vision.

Graph neural networks (GNNs) extend the performance benefits of deep learning to graph data. Like other popular neural networks, a GNN model has a series of layers, which progress toward higher levels of abstraction.

For instance, the first layer of a GNN computes a representation — or embedding — of the data represented by each node in the graph, while the second layer computes a representation of each node based on the prior embedding and the embeddings of the node’s nearest neighbors. In this way, every layer expands the scope of a node’s embedding, from one-hop neighbors, to two-hop neighbors, and for some applications, even further.

Graph neural network
A demonstration of how graph neural networks use recursive embedding to condense all the information in a two-hop graph into a single vector. Relationships between entities — such as "produce" and "write" in a movie database (red and yellow arrows, respectively) — are encoded in the level-0 embeddings of the entities themselves (red and orange blocks).
Stacy Reilly

GNN tasks

The individual node embeddings can then be used for node-level tasks, such as predicting properties of a node. The embeddings can also be used for higher-level inferences. For instance, using representations across a pair of nodes or across all nodes from the graph, GNNs can perform link-level or graph-level tasks, respectively.

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In this section, we demonstrate the versatility of GNNs across all three levels of tasks and examine how our customers are using GNNs to tackle a variety of problems.

Node-level tasks

Using GNNs, we can infer the behavior of an individual node in the graph based on the relationships it has to other nodes. One common task is node classification, where the objective is to infer nodes’ missing labels by looking at their neighbors’ labels and features. This method is used in applications such as financial-fraud detection, publication categorization, and disease classification.

In AWS, we have successfully used Amazon Neptune and Deep Graph Library (DGL) to apply GNN node representation learning to customers’ fraud detection use cases. For a large e-commerce sports gadgets customer, for instance, scientists in the Amazon Machine Learning Solutions Lab successfully used GNN models implemented in DGL to detect malicious accounts among billions of registered accounts.

Fraud graph.png
An example of how a graph representation can be used to detect fraud.

These malicious accounts were created in large quantities to abuse usage of promotional codes and block general public access to the vendor’s best-selling items. Using data from e-commerce sites, we built a massive heterogenous graph in which the nodes represented accounts and other entities, such as products purchased, and the edges connected nodes based on usage histories. To identify malicious accounts, we trained a GNN model to propagate labels from accounts that were known to be malicious to unlabeled accounts.

With this method, we were able to detect 10 times as many malicious accounts as a previous rule-based detection method could. Such performance improvements could not be achieved by traditional methods for doing machine learning on tabular datasets, such as CatBoost, which take only account features as inputs, without considering the relationships between accounts captured by the graph.

Besides applications for inherently relational, graph-structured data, such as social-network and citation-network data, there have been extensions of GNNs for data normally presented in Euclidean space, such as images and texts. By transforming data in Euclidean space to graphs based on spatial proximity, GNNs can solve problems that are typically solved by convolutional neural networks (CNNs) and recurrent neural networks (RNNs), which were designed to handle visual data and sequential data.

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For example, researchers have explored GNN models to improve the accuracy of information extraction, a task typically handled by RNNs. GNNs turn out to be better at incorporating the nonlocal and nonsequential relationships captured by graph representations of word dependencies.

In a recent collaboration, the Amazon Machine Learning Solutions Lab and United Airlines developed a customized GNN model (DocGCN) to improve the accuracy of automatic information extraction from self-uploaded passenger documents, including travel documents, COVID-19 test results, and vaccine cards. The team built a graph for each scanned travel document that connected textual units based on their spatial proximities and orientations in the document.

Then, the DocGCN model reasoned over the relationships among textual units (nodes of the graph) to improve the identification of relevant textual information. DocGCN also generalized to complex forms with different formats by leveraging graphs to capture relationships between texts in tables, key-value pairs, and paragraphs. This improvement expedited the automation of international travel readiness verification.

Link-level tasks

Another important learning task in graphs is link prediction, which is central to applications such as product or ad recommendation and friendship suggestion. Given two nodes and a relation, the goal is to determine whether the nodes are connected by the relation.

Typically, the prediction is provided by a decoder that consumes the embeddings of the source and destination nodes, as in the work on knowledge graph embedding at scale that members of our team presented at SIGIR 2020. The decoder is trained to correctly predict existing edges in the graph.

DRKG.png
The high-level structure of DRKG. Numerals indicate the number of different types of relationships between classes of entities; terms between parentheses are examples of those relationships.
Credit: Glynis Condon

An exciting opportunity area in this context is drug discovery. AWS has recently provided a drug-repurposing knowledge graph (DRKG) that employs link prediction to identify new targets for existing drugs. Built by scientists at AWS, DRKG is a comprehensive biological knowledge graph that relates human genes, chemical compounds, biological processes, drug side effects, diseases, and symptoms. By performing link prediction around COVID-19 in DRKG, researchers were able to identify 41 drugs that were potentially effective against COVID-19 — 11 of which were already in clinical trials.

AWS also publicly released this solution, built by leveraging DRKG, as the COVID-19 Knowledge Graph (CKG). CKG organizes and represents the information in the COVID-19 Open Research Dataset (CORD-19), enabling fast discovery and prioritization of drug candidates. It can also be employed to identify papers relevant to COVID-19, thereby reducing the scale of human effort required to study, summarize, and interpret findings relevant to the pandemic.

Graph-level tasks

Graph-level tasks involve the analysis of large collections of small and independent graphs. A chemical library of organic compounds is a common example of a graph-level application, where each organic compound is represented as a graph of atoms connected by chemical bonds. Graph-level analyses of chemical libraries are often vital for drug development and discovery use cases; applications include predicting organic compounds’ chemical properties and predicting biological activities such as binding affinity to protein targets.

Code graph.png
An example of a program dependence graph.

Another example of data that can benefit from graph-level representation is code snippets in programming languages. A piece of code can be represented by a program dependence graph (PDG), where variables, operators, and statements are nodes connected by their dependencies (links).

At PAKDD 2021, we presented a new method for using GNNs to represent code snippets. Recently, we have been using that method to identify similar code snippets, to find opportunities to make code more modular and easier to maintain.

GNNs can also be used to encode global properties of the underlying systems and incorporate them into graph embeddings, in a way that is difficult with other deep-learning methods. We recently worked with scientists from Janssen Biopharmaceuticals to predict the function of proteins from their 3-D structure, which is useful for research and development in the pharmaceutical and biotech industries.

A protein is composed of a sequence of amino acids folded in a particular way. We developed a graph representation of proteins in which each node was an amino acid, and the interactions between amino acids in the folded protein structure determined whether two nodes were linked or not.

Protein graphs.png
Examples of graph representations of proteins.

This allowed us to encode fine-grained biological information, including the distance, angle, and direction of contact between neighboring amino acid residues. When we combined a GNN trained on these graph representations with a model trained to parse billions of protein sequences, we improved performance on various protein function prediction tasks of real-world importance.

Graph-level tasks for GNNs have different data-engineering requirements than the previous tasks. Node-level and link-level tasks usually operate on a single giant graph, whereas graph-level tasks operate on a large number of independent small graphs.

To help customers scale GNNs up for graph-level tasks, we developed a cloud-based architecture that leverages the highly performant open-source GNN library DGL, the ML resource orchestration tool SageMaker, and Amazon DocumentDB for managing graph data.

Getting started on your GNN journey

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In this article, we presented a few examples of GNN applications at all three levels of graph-related tasks to showcase the value of GNNs to various enterprise and research problems. AWS provides several options for customers looking to build and deploy GNN-powered ML solutions. Customers looking to get started quickly can use Amazon Neptune ML to build GNN models directly on graph data stored in Amazon Neptune without writing any code. Amazon Neptune ML can train models to tackle node-level and link-level tasks like those described above. Customers looking to get more hands-on can implement GNN models using DGL on Amazon SageMaker. In the meantime, we will continue to advance the science of GNNs to build more products and solutions to make GNNs more accessible to all our customers.

Acknowledgments: Guang Yang, Soji Adeshina, Jasleen Grewal, Miguel Romero Calvo, Suchitra Sathyanarayana

Research areas

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Shape the Future of Cloud Computing Are you a graduate student passionate about Automated Reasoning and its real-world applications? Join our team of innovators and embark on a journey to revolutionize cloud computing through cutting-edge automated reasoning techniques.Our tools are called billions of times daily, powering the backbone of Amazon's products and services. We are changing the way computer systems are developed and operated, raising the bar for security, durability, availability, and quality. As an Applied Science Intern, you'll have the opportunity to work alongside our brilliant scientists and contribute to groundbreaking projects. From distributed proof search and SAT/SMT solvers to program analysis, synthesis, and verification, you'll tackle complex challenges at the intersection of theory and practice, driving innovation and delivering tangible value to our customers. This internship is not just about executing tasks – you'll explore novel approaches to solving intricate automated reasoning problems. You'll dive deep into cutting-edge research, leveraging your expertise to develop innovative solutions. You'll work on deploying your solutions into production, witnessing the real-world impact of your contributions. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment. Join us and be part of a team that is shaping the future of cloud computing through the power of Automated Reasoning. Apply now and unlock your potential! Amazon has positions available for Automated Reasoning Applied Science Internships in, but not limited to, Arlington, VA; Boston, MA; Cupertino, CA; Minneapolis, MN; New York, NY; Portland, OR; Santa Clara, CA; Seattle, WA; Bellevue, WA; Santa Clara, CA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. Key job responsibilities We are particularly interested in candidates with expertise in: Theorem Proving, Boolean Satisfiability Solvers, Bounded Model Checking, Deductive Verification, Programming/Scripting Languages, Abstract Interpretation, Automated Reasoning, Static/Program Analysis, Program Synthesis In this role, you will work alongside global experts to develop and implement novel, scalable algorithms and modeling techniques that advance the state-of-the-art in areas at the intersection of Natural Language Processing and Speech Technologies. You will tackle challenging, groundbreaking research problems on production-scale data, with a focus on natural language processing, speech recognition, text-to-speech (TTS), text recognition, question answering, NLP models (e.g., LSTM, transformer-based models), signal processing, information extraction, conversational modeling, audio processing, speaker detection, large language models, multilingual modeling, and more. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment.
US, WA, Seattle
Unleash Your Potential as an AI Trailblazer At Amazon, we're on a mission to revolutionize the way people discover and access information. Our Applied Science team is at the forefront of this endeavor, pushing the boundaries of recommender systems and information retrieval. We're seeking brilliant minds to join us as interns and contribute to the development of cutting-edge AI solutions that will shape the future of personalized experiences. As an Applied Science Intern focused on Recommender Systems and Information Retrieval in Machine Learning, you'll have the opportunity to work alongside renowned scientists and engineers, tackling complex challenges in areas such as deep learning, natural language processing, and large-scale distributed systems. Your contributions will directly impact the products and services used by millions of Amazon customers worldwide. Imagine a role where you immerse yourself in groundbreaking research, exploring novel machine learning models for product recommendations, personalized search, and information retrieval tasks. You'll leverage natural language processing and information retrieval techniques to unlock insights from vast repositories of unstructured data, fueling the next generation of AI applications. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Join us at the forefront of applied science, where your contributions will shape the future of AI and propel humanity forward. Seize this extraordinary opportunity to learn, grow, and leave an indelible mark on the world of technology. Amazon has positions available for Machine Learning Applied Science Internships in, but not limited to Arlington, VA; Bellevue, WA; Boston, MA; New York, NY; Palo Alto, CA; San Diego, CA; Santa Clara, CA; Seattle, WA. Key job responsibilities We are particularly interested in candidates with expertise in: Knowledge Graphs and Extraction, Programming/Scripting Languages, Time Series, Machine Learning, Natural Language Processing, Deep Learning,Neural Networks/GNNs, Large Language Models, Data Structures and Algorithms, Graph Modeling, Collaborative Filtering, Learning to Rank, Recommender Systems In this role, you'll collaborate with brilliant minds to develop innovative frameworks and tools that streamline the lifecycle of machine learning assets, from data to deployed models in areas at the intersection of Knowledge Management within Machine Learning. You will conduct groundbreaking research into emerging best practices and innovations in the field of ML operations, knowledge engineering, and information management, proposing novel approaches that could further enhance Amazon's machine learning capabilities. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Design, implement, and experimentally evaluate new recommendation and search algorithms using large-scale datasets - Develop scalable data processing pipelines to ingest, clean, and featurize diverse data sources for model training - Conduct research into the latest advancements in recommender systems, information retrieval, and related machine learning domains - Collaborate with cross-functional teams to integrate your innovative solutions into production systems, impacting millions of Amazon customers worldwide - Communicate your findings through captivating presentations, technical documentation, and potential publications, sharing your knowledge with the global AI community
US, WA, Seattle
Do you have a strong science background and want to help build new technologies? Do you have a physics background and want to help build and test superconducting circuits? Would you love to help develop the algorithms and models that power computer vision services at Amazon, such as Amazon Rekognition, Amazon Go, Visual Search, etc? Join the quantum revolution at Amazon and be part of a team that's pushing the boundaries of what's possible in quantum computing and quantum technologies. As a Research Science Intern focused on Quantum Technologies, you'll have the opportunity to work alongside leading experts in the field, contributing to cutting-edge research and driving innovation in areas such as quantum algorithms, quantum simulation, superconducting qubits, quantum key distribution, and quantum optics. We are looking for builders, innovators, and entrepreneurs who want to bring their ideas to reality and improve the lives of millions of customers. Research interns at Amazon work passionately to apply cutting-edge advances in technology to solve real-world problems. As an intern, you will be challenged to apply theory into practice through experimentation and invention, develop new algorithms using modeling software and programming techniques for complex problems, implement prototypes and work with massive datasets. Throughout your journey, you'll have access to unparalleled resources, including state-of-the-art computing infrastructure, cutting-edge research papers, and mentorship from industry luminaries. This immersive experience will not only sharpen your technical skills but also cultivate your ability to think critically, communicate effectively, and thrive in a fast-paced, innovative environment where bold ideas are celebrated. Amazon has positions available for Operations Research Science Internships in, but not limited to, Bellevue, WA; Boston, MA; Cambridge, MA; New York, NY; Santa Clara, CA; Seattle, WA; Sunnyvale, CA. Key job responsibilities We are particularly interested in candidates with the following skills: Quantum Algorithms, Quantum Simulators, Superconducting Qubits, Quantum Key Distribution , Optics In this role, you ain hands-on experience in applying cutting-edge analytical techniques to tackle complex business challenges at scale. If you are passionate about using data-driven insights to drive operational excellence, we encourage you to apply. The ideal candidate should possess the ability to work collaboratively with diverse groups and cross-functional teams to solve complex business problems. A successful candidate will be a self-starter, comfortable with ambiguity, with strong attention to detail and the ability to thrive in a fast-paced, ever-changing environment. A day in the life - Conduct research and develop new quantum algorithms to solve complex computational problems - Design and implement quantum simulation models to study the behavior of quantum systems - Investigate the properties and performance of superconducting qubits, a promising platform for building large-scale quantum computers - Explore the application of quantum key distribution protocols for secure communication and data encryption, ensuring the privacy and integrity of sensitive information - Explore the application of quantum optics principles to develop novel quantum sensing and communication technologies