Scaling Trust & Detecting Outliers with Graph Neural Networks

Our world is increasingly fueled by AI-driven decision-making, so trustworthy data is non-negotiable. 

When algorithms determine who gets a loan, who passes a fraud screening, or which transactions are flagged for investigation, organizations must trust that these decisions are not only accurate but also explainable and fair. Traditional machine learning models often fall short of this standard, especially when the data is complex and highly interconnected. That’s where Graph Neural Networks (GNNs) come in—and where TigerGraph is leading the charge.

Neural networks have a reputation for being “black boxes” that don’t explain their predictions, but GNNs provide a path to explanatory models. Because they learn from relationships, not just attributes, their predictions can be traced back through the network of connections that influenced them. When combined with tools like attention layers or graph-based query inspection, this makes it possible to understand not just what a model predicted, but why—a critical step for building trust in AI systems.

Why Traditional Models Aren’t Enough

Most machine learning models analyze tabular data—discrete slices of information, such as income, age, or transaction history. And they make predictions based on these isolated features, but real-world behaviors don’t happen in isolation. They unfold in networks of relationships between accounts, devices, suppliers, and more.

Without properly modeling these relationships, organizations risk:

• False negatives: Fraudsters cleverly hide in complex transaction networks. GNNs catch these hidden connections by understanding multi-hop relationships that are invisible in flat data.
• False positives: Legitimate customers are denied based on incomplete views of their behavior. Traditional models can only see isolated points, while GNNs analyze relational context to reduce false positives.
• Bias reinforcement: Overfitting to skewed data patterns without understanding the broader context. GNNs mitigate this by uncovering patterns across entire networks, not just isolated attributes.

Graph-powered analytics solve these challenges by making connections first-class citizens in the data model. In TigerGraph, this is optimized at scale with distributed processing, ensuring that even multi-hop paths across billions of nodes are traversed in real time. The relationships are treated as primary, queryable objects within the database, not just implied links. 

This means edges (connections) are directly accessible and traversable, enabling seamless multi-hop analysis that would otherwise require complex joins in traditional models. GNNs extend this power even further by learning from the structure of the graph itself—not just attributes, but the relationships between them.

What Graph Features and GNNs Bring to the Table

Graph-enhanced ML represents a significant leap forward in machine learning, as it learns not only from attributes but also from relationships. In first generation graph-enhanced learning, graph features such as PageRank and betweenness centrality are added to the training data, resulting in better accuracy and explainability, with proven results for use cases like financial fraud detection.. These graph features provide deeper visibility into network behavior:

• PageRank: Measures the influence or importance of a node within a network. In fraud detection, it surfaces central accounts in money-laundering rings or fraud rings, identifying the primary hubs where suspicious activity is coordinated. Unlike other graph databases, TigerGraph’s parallel processing speeds up PageRank calculations, even over billions of nodes, ensuring fraud detection is not just accurate, but real-time.
• Betweenness Centrality: Detects key intermediaries that serve as bridges in transaction pathways. In complex schemes, fraudulent accounts may not always initiate transactions but instead act as brokers or middlemen. Betweenness centrality helps locate these critical connectors, enabling earlier disruption of coordinated activities. TigerGraph’s unique in-memory parallelism allows it to compute these paths much faster than traditional graph databases, highlighting hidden pathways in milliseconds instead of minutes.

These features allow models to predict fraudulent behavior not just from isolated attributes, but from understanding influence and connectivity within the network. This is crucial for identifying hidden relationships and breaking fraud chains before they escalate. 

TigerGraph-trained GNNs are the next generation of ML, going even deeper:

• • They “convolve” over neighborhoods, learning hidden patterns across connected nodes. This is a process similar to how Convolutional Neural Networks (CNNs) process image data. In a CNN, the model scans through pixels in small grids, understanding spatial relationships. GNNs do the same with graph data—aggregating information from immediate neighbors, learning about the structure, and propagating this information through the network. This allows the model to detect multi-hop patterns like fraud rings or covert money transfers that traditional models would overlook.

• They generalize better across complex, changing networks where explicit rules fail. This is because GNNs do not rely on static features—they continuously learn from evolving connections. For example, in cybersecurity, the network topology of attacks is constantly evolving. GNNs adapt by updating their understanding of how nodes relate to one another, even as new threats emerge. This dynamic learning process allows GNNs to catch previously unseen fraud or network threats that rule-based models would miss entirely.

• They surface anomalies—not just simple outliers—that standalone attribute models miss entirely. This happens because GNNs leverage the graph’s structure to understand relationships and multi-hop paths that would be invisible in isolated attribute-based models. For example, a series of small transactions might seem benign individually, but when analyzed in the context of multi-hop relationships, they can reveal a money-laundering scheme or coordinated fraud ring. Traditional models treat these as disconnected points, while GNNs surface the hidden structure behind them.

• They offer both accuracy and explainability. Because their predictions are based on relationships and properties, a prediction can be deconstructed to see which relationships were the most influential in reaching that decision. 

Understanding the Difference: Anomalies vs. Outliers

An outlier is a single data point that deviates from the norm (e.g., a single unusually large transaction). In contrast, an anomaly is a deviation within the structure or group behavior that is fundamentally different from the norm (e.g., a network of accounts interacting in non-standard ways). In other words, an outlier is an unusual outcome that may or may not have an unusual cause, whereas an anomaly is an event that is not explainable by ordinary behavior.

TigerGraph’s Hybrid Graph + Vector Search is purpose-built to identify both:

• Vector Search detects outliers—isolated points that are dissimilar to known patterns.
• Graph Search identifies anomalies—relational disruptions or hidden structures across multi-hop relationships.

This dual-layered approach enables a more granular and more explanation-based detection method that identifies both isolated irregularities and deeper structural fraud.

Why Traditional Databases Struggle with Relationships

Traditional databases like relational (SQL) and NoSQL systems are not designed to treat relationships as first-class citizens. In SQL, relationships are represented through foreign keys and require expensive joins to navigate connections. For example, understanding how a single account is linked to multiple fraudulent transactions across banks can require joining several tables, which dramatically slows down query speed.

NoSQL databases, like MongoDB or Cassandra, are optimized for document storage but treat relationships as secondary, often requiring manual stitching or external processing to understand multi-hop paths. This is why they struggle with real-time, multi-layered fraud detection or complex supply chain mapping.

TigerGraph is different: its graph-native storage makes edges (connections) primary objects. This allows for instant traversal across multiple hops, even at massive scale. In TigerGraph, relationships are direct, queryable, and optimized for real-time analysis—making anomaly detection faster and more efficient.

Making GNNs Work at Scale

Many platforms talk about GNNs—but TigerGraph makes them enterprise-ready. Unlike traditional graph databases, TigerGraph is purpose-built to scale with parallel traversal across billions of nodes. Here’s why:

• Speed and Scale: Native parallelism and distributed architecture allow massive graphs—hundreds of millions or even billions of connections—to be traversed and processed in real time. Traditional databases struggle or resort to costly workarounds.
• Direct Graph Integration: Rather than flattening a graph into a table, which destroys important structure, TigerGraph enables seamless feature extraction, graph querying, and GNN training. This preserves the rich relationships that power better models.
• Enterprise Readiness: TigerGraph is designed with the enterprise software features that businesses demand for maintenance and reliability, such as fine-grained access control and high availability with automatic failover.
• Data Science Friendly: Its pyTigerGraph Python library simplifies graph operations and presents them in the language of choice for data scientists – Python – so they focus on design and tuning models, without learning another graph query language.

And importantly, TigerGraph isn’t just “handling” graphs—it’s purpose-built to amplify graph-native intelligence. Its algorithmic computation (as opposed to just in-graph traversal) means that heavy analytics, like PageRank and community detection, execute in real time—no pre-computation required, delivering what our customers recognize as real-time, massively scalable, graph-powered machine learning.

Building More Trustworthy AI

Deploying GNNs on TigerGraph is about building AI systems people can trust, offering explainability, fairness, and adaptability.

• Explainability: Visualize how an account’s relationships contribute to a fraud score—no black box, just clear logic.
• Fairness: Detect anomalies based on behavior across networks, not biased assumptions.
• Adaptability: Models keep pace with evolving fraud tactics, customer behaviors, or cyber threats.

In a world where AI-driven decisions impact real lives, scaling trust is crucial. GNNs, powered by TigerGraph, make it possible.

Ready to scale trust in your AI models? Learn how our ML Workbench and graph-native infrastructure can help you uncover deeper insights and make smarter, fairer decisions faster.