Building Trustworthy Graph Neural Networks

Artificial intelligence (AI) is rapidly transforming our world, from recommending products to diagnosing diseases. Indeed, Graph Neural Networks (GNNs), a type of AI that excels at analyzing complex relationships within graph data, can be used for a number of applications such as:

• Recommending products or content on social media and e-commerce platforms.
• Detecting fraud and anomalies in financial transactions and network activity.
• Discovering new drugs and materials in scientific research.
• Analyzing social networks and interactions to understand human behavior.

Given that AI and GNNs are increasingly being used in ways that can have a significant social impact and can influence an individual’s life chances, such as in medicine, it’s crucial to ensure these systems are trustworthy. In this blog post, we give an overview of how the power of GNNs is being harnessed, how they can be implemented, and best practices to increase the trustworthiness of GNNs.

What are Graph Neural Networks?

Graphs (or networks) are data structures that are present in different contexts of biology, engineering, physics, and economics that are composed from two fundamental elements: nodes and edges. Here, nodes represent entities, and the edges represent the relationships between those entities. Using this combination of nodes and edges, GNNs can be used to represent multiple types of data, including:

• Technological networks: internet, telephone network, power grids, transportation networks, delivery networks
• Networks of information: the World Wide Web, citation networks, peer-to-peer networks, recommendation networks
• Social networks: online social networks (Facebook, Instagram, Bluesky), offline social networks (networks formed during a pandemic, friends, work)
• Biological networks: biochemical networks, brain networks, ecological networks

We can illustrate a transportation network with the London underground map, where stations represent the nodes, and the tube lines between stations represent the edges. From the GNN, optimization algorithms can be applied in this context to evaluate, for example, which is the better route for travel from a given station A to station B.

What are GNNs used for?

Graph Neural Networks (GNNs) have emerged as powerful tools in various real-world applications, and,  as research and development in trustworthy GNNs progresses, we can expect even wider adoption and impact across diverse fields.

Examples of their applications include social recommendation, where platforms like Pinterest leverage GNNs like PinSage to personalize content suggestions based on user interests and social connections. They can also be used for traffic prediction by modelling traffic flow within road networks, enabling optimized traffic management strategies.

GNNs also play a crucial role in fraud detection by analyzing financial transaction graphs to identify anomalous patterns. This can help to facilitate early interventions where fraud is detected and prevent individuals becoming victim to financial crime, and even prevent fraud in the first place. Elsewhere, the pharmaceutical industry utilizes GNNs for drug discovery, where they analyze molecular structures to predict the properties of potential drug candidates.

Furthermore, GNNs have applications in natural language processing, where they analyze relationships between words and sentences to enhance tasks such as sentiment analysis and machine translation. These applications, in turn, have a wide range of uses, from marketing to immigration.

These diverse applications demonstrate the versatility and effectiveness of GNNs in solving complex problems across various domains.

How are Graph Neural Networks implemented?

Implementing a GNN involves several steps. Broadly, they take the form of:

1. Data Preparation: Represent your data as a graph with nodes and edges, including relevant features for both.

2. Model Selection: Choose a suitable GNN architecture based on your task and data characteristics. Popular architectures include Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and Graph Recurrent Networks (GRNs).

3. Model Training: Train the GNN model on your prepared graph data, optimizing its parameters to minimize prediction errors.

4. Evaluation and Inference: Evaluate the trained model’s performance on a separate test dataset and then use it to make predictions on new, unseen data.

Several popular frameworks and libraries facilitate GNN implementation, including:

• PyTorch Geometric (PyG): A widely used Python library offering various GNN architectures, data loaders, and utilities.
• Deep Graph Library (DGL): Another popular Python library providing efficient implementations of GNNs and supporting diverse graph tasks.
• TensorFlow GNN: A TensorFlow-based library offering GNN components and functionalities for building and training GNN models.

The current limitations of GNNs

While GNNs offer great potential, they also raise concerns about potential harms like:

• Vulnerability to adversarial attacks: Malicious actors can manipulate graph data to fool GNNs, leading to incorrect or harmful predictions.
• Bias or discrimination: GNNs can perpetuate biases hidden within data, leading to unfair outcomes for certain groups or individuals.
• Privacy breaches: Sensitive information within graph data can be leaked or inferred from GNN models.
• Excessive resource consumption: Training and deploying GNNs can be computationally expensive and energy-intensive, impacting the environment.

How can GNNs be made more trustworthy?

Researchers are now actively exploring ways to build trustworthy GNNs to address these concerns, focusing on six key aspects:

• Robustness: Making GNNs resistant to adversarial attacks and random errors.
• Explainability: Developing methods to understand and explain GNN predictions, making them more transparent.
• Privacy: Implementing techniques to protect confidential data within GNN models and graph data.
• Fairness: Ensuring GNNs treat individuals and groups fairly, regardless of sensitive attributes like race or gender.
• Accountability: Establishing clear lines of responsibility for GNN system behavior and providing mechanisms to detect and address violations.
• Environmental Well-being: Improving the efficiency of GNNs to reduce their resource consumption and environmental impact.

Building trustworthy GNNs requires a shift in focus from solely pursuing high performance to prioritizing these trust-oriented characteristics. This involves developing new methods and adapting existing techniques to the unique challenges of graph data.

Furthermore, it’s crucial to recognize the interplay between different aspects of trustworthiness. For example, improving explainability can help identify vulnerabilities and design more robust GNNs. Similarly, techniques for ensuring fairness can also enhance privacy by reducing the risk of sensitive information leakage.

Take steps to increase trust in AI

Research on trustworthy GNNs is still in its early stages, but it’s a rapidly growing field with significant potential. By developing GNNs that are not only accurate but also robust, explainable, privacy-preserving, fair, accountable, and environmentally friendly, we can ensure that these powerful AI systems are used responsibly and ethically for the benefit of society.

Schedule a demo to find out how Holistic AI can help you increase the trust in your AI.