Graph Neural Networks: When Relationships Are the Signal

Most machine learning problems are still prepared as tables. Each row becomes an example. Each column becomes a feature. The model learns patterns from the attributes attached to each record, then applies what it learned to a new record it hasn’t seen before.

That structure works when the signal lives mostly inside the row, but it starts to break down when the signal lives between rows — the relationship between them.

Many real-world systems aren’t collections of independent records. They’re networks of connected entities, where the meaning of one entity depends on how it’s linked to others. For example, a transaction might be high-risk because of the accounts, devices and counterparties around it.

Traditional feature engineering can try to summarize those relationships: number of shared devices, distance from a flagged account, count of upstream suppliers, similarity to known molecular structures. But those summaries are usually hand-built, task-specific and incomplete. They compress a larger network into a fixed set of columns before the model ever sees it.

Graph neural networks were developed to address this class of problems. They give deep learning models a way to learn from graph structure directly, so predictions can account for the relationships among entities rather than treating every entity as independent.

A graph neural network, or GNN, is a deep learning architecture for graph-structured data. In a graph, nodes represent entities and edges represent relationships between them. Those entities might be users, accounts, products, suppliers, transactions, atoms, documents or devices. The edges describe how those entities connect.

A simple graph might show customers connected to the products they buy. A more complex graph might connect users to accounts, accounts to devices, devices to IP addresses and IP addresses to transactions. In a molecular graph, for example, atoms are nodes and chemical bonds are edges. In a supply chain graph, manufacturers, suppliers, ports, carriers and distribution centers are connected parts of a larger operating network.

The core idea behind a GNN is that an entity’s meaning depends partly on its neighborhood. Instead of learning only from the features attached to one node, the model learns by aggregating information from surrounding nodes. Over training, it produces node embeddings — learned numerical representations that encode both the node’s own features and the structure around it.

This makes GNNs useful for problems where relationships carry predictive signal. Common applications include fraud detection, drug discovery, recommendations, entity resolution, supply chain analysis and knowledge graph reasoning.

To understand GNNs, you have to understand these concepts:

• A graph is a structure made of nodes and edges.
• A node is an entity in the graph, such as an account, product, customer or supplier.
• An edge is a relationship between nodes, such as “purchased,” “transferred to,” “shares device with,” “ships through” or “bonded to.”
• An adjacency matrix is a mathematical representation of graph connections. It records which nodes are connected to which other nodes.
• A node embedding is the learned vector representation of a node. In a GNN, that representation reflects not only the node’s own features but also information gathered from its neighbors.

Graph neural networks work by letting information move through a graph. The most common mechanism is called message passing, which is why many GNNs are also described as message passing neural networks.

In a message passing neural network, each node receives information from its neighbors, combines that information and updates its own representation. The process usually follows three steps: collect, aggregate and update.

• First, each node collects messages from neighboring nodes: Those messages are based on neighbor features, edge features or learned transformations of both.
• Next, the model aggregates the incoming messages: Because a node can have many neighbors, the model needs a way to combine multiple signals into a single representation. This process is often described as neighborhood aggregation: each node updates its representation by combining information from the nodes around it. Some GNNs use a mean or sum. Others use learned weights to decide which neighbors should matter more.
• Finally, the node updates its own embedding using the aggregated neighborhood information and its previous representation: After one layer of message passing, a node has information from its immediate neighbors. After two layers, it typically has information from neighbors of neighbors. With each additional layer, the model’s receptive field expands farther across the graph.

That layered propagation is what lets a GNN detect patterns that aren’t visible inside a single record. In entity resolution, two records may not match exactly, but their surrounding relationships may reveal that they refer to the same customer, organization or asset.

This same mechanism also introduces one of the central technical challenges in GNN design: over-smoothing. As more layers are stacked, node representations can begin to converge. Instead of preserving meaningful differences between nodes, the model makes them look increasingly similar. A shallow GNN may miss long-range structure, but a deep GNN can blur distinctions the model needs to make accurate predictions.

Training depends on the task:

• In node classification, the model predicts a label for individual nodes: whether an account is fraudulent, whether a document belongs to a topic or whether a customer belongs to a segment.
• In link prediction, the model predicts whether an edge should exist between two nodes: whether a user is likely to buy a product, whether two records refer to the same entity or whether a transaction relationship is suspicious.
• In graph classification, the model predicts a label for an entire graph, such as whether a molecule has a desired property.

The architecture is different from a standard neural network, but the learning problem remains familiar. The model receives training examples, compares predictions with known labels or observed relationships, and adjusts its parameters to reduce error. What changes is the structure of the input. The model isn’t only learning from feature values — it’s learning from how those values are connected.

Graph neural networks are a broad family of architectures. Most variants learn by aggregating neighborhood information, but they differ in how messages are passed, weighted and scaled.

Graph convolutional networks

A graph convolutional network, or GCN, extends the idea of convolution from regular grids to graph structures. In a convolutional neural network, a filter aggregates information from nearby pixels in an image. In a GCN, each node aggregates information from nearby nodes in a graph.

GCNs address irregularity in a way that CNNs can’t. Because images sit on fixed grids, each pixel has a predictable spatial arrangement. But graphs are irregular: one node might have two neighbors, while another may have 200 or might be isolated. A GCN handles that irregularity by using normalized neighbor aggregation, so information from connected nodes can be combined in a stable way.

GCNs are often a good starting point because they make the local-aggregation idea easy to understand. They’re widely used for node classification, graph representation learning and semi-supervised learning on graph data.

Graph attention networks

A graph attention network, or GAT, adds learned attention to neighborhood aggregation. Instead of treating all neighbors equally, a GAT learns which neighboring nodes should contribute more to a node’s updated representation.

This is useful when some relationships are more informative than others. In a recommendation graph, for example, a recent interaction may mean more than an old one.

Careful graph construction is still required, but a GAT approach gives the model a more flexible way to weigh neighborhood information.

GraphSAGE

GraphSAGE was designed for scalability. In very large graphs, aggregating information from every neighbor can become expensive or impractical. GraphSAGE addresses this problem by sampling a fixed-size set of neighbors during training, making it easier to train on graphs with millions or billions of nodes. It also supports inductive learning, where the model can generate embeddings for nodes it didn’t see during training, as long as those nodes have features and connections the model can use.

This is especially important in enterprise settings, where graphs are rarely static. New customers, accounts, devices, suppliers, products and transactions appear constantly. A graph model needs a path for updating representations as the graph changes.

Message passing neural networks

A message passing neural network, or MPNN, is a general framework that describes many GNN architectures. The details vary, but there’s a central pattern: nodes exchange messages with neighbors, aggregate incoming information and update their representations.

This framework is especially common in scientific and molecular modeling, where a molecule is represented as a graph, with atoms as nodes and bonds as edges. Message passing lets the model learn how local chemical structure contributes to a molecule’s overall properties.

Graph transformers

Newer graph architectures also adapt transformer-style attention to graph data. A graph transformer uses attention mechanisms to model relationships across nodes, often with additional structural information so the model can understand graph topology.

Graph transformers can help with long-range dependencies that are difficult for shallow message passing networks to capture. They can also be more computationally demanding. As with other GNN variants, the right architecture depends on the graph size, task, available labels, latency requirements and the importance of interpretability.

GNNs are best used when the relationship pattern changes the prediction. If a model can learn everything it needs from tabular features, a graph neural network typically just adds complexity without improving performance. But when the connections among entities carry important context, graph representation learning can reveal relational structure that may be difficult for tabular approaches to capture.

Fraud detection

Fraud is typically signaled by coordinated behavior across accounts, cards, devices, phone numbers, IP addresses, merchants, addresses or payment instruments. A single transaction may look normal in isolation, but the behavior around it will often reveal suspicious repetition or coordination.

GNNs can model those relationships directly. Message passing enables the model to propagate learned information across connected entities, allowing relational patterns to contribute to predictions.

Drug discovery and molecular property prediction

In molecular modeling, the arrangement matters as much as the ingredients. A model needs to account for which atoms are present, how they’re bonded and how those local bond patterns contribute to the molecule’s larger behavior.

GNNs can use that structure to predict properties such as solubility, toxicity, binding affinity or biological activity. Instead of relying primarily on hand-engineered molecular descriptors, the model learns representations from the graph itself. Message passing allows information to move across atoms and bonds, helping the model capture how local chemical structures contribute to larger molecular behavior.

This use case is one of the clearest examples of where graph structure can provide additional modeling context. The same atoms arranged differently can produce very different properties. The connections themselves are part of the object being modeled.

Recommendation systems

Recommendation systems often need to infer preference from patterns of interaction. A user’s next best product, article or video may not be obvious from that user’s profile alone. It may only emerge from the behavior of similar users, the items those users engaged with and the paths that connect users, products, categories and sessions across the interaction graph.

A GNN can represent users and items as nodes in a graph, with edges representing clicks, purchases, ratings, views or other interactions. Message passing lets the model learn from nearby and multi-hop patterns: users with similar behavior, products often purchased together or content that sits close in the interaction graph.

This can be especially useful when recommendations need to account for sparse data. For example, when a user doesn’t have many direct interactions, but their position in the graph can still provide context.

Entity resolution and knowledge graphs

Entity resolution determines when different records refer to the same real-world entity. In enterprise data, the same customer, supplier, product or account may appear under different names, identifiers or formats across systems.

A purely tabular approach can compare record attributes, such as names, addresses, emails, tax IDs, domains and other fields, but it stops there. A graph-based approach can also compare the relationships around those attributes. Two company records may not match exactly, but they may share executives, addresses, contracts, subsidiaries, devices, domains or transaction patterns. Those connections can provide strong evidence that the records belong together.

This becomes more powerful when combined with a knowledge graph. A knowledge graph connects business entities and relationships into a shared structure, such as customers, products, suppliers, contracts, policies, support cases, shipments, claims or other domain objects. GNNs can learn representations over that structure for classification, link prediction, entity matching or recommendation tasks.

For enterprises, this is where GNNs connect naturally to data platform strategy. The graph is only as useful as the underlying entity data, relationship data and governance around it.

Supply chain analysis

Supply chains are networks by design. Suppliers connect to manufacturers, manufacturers connect to logistics providers, logistics providers connect to ports, warehouses and distribution centers. Disruptions or dependencies can propagate through those connections.

A GNN can help model dependencies by learning from the supplier’s position in the broader network: upstream concentration, shared chokepoints, geographic exposure, carrier dependencies or indirect relationships to disrupted entities.

That doesn’t make supply chain optimization simple, of course. Graph construction, data quality and changing real-world conditions all remain factors. But the graph structure maps more closely to the problem than a flat table of supplier attributes alone.

Graph neural networks and convolutional neural networks share an important idea: local aggregation. Both architectures learn by combining information from nearby elements. The difference is the structure of “nearby.”

• A convolutional neural network, or CNN, is designed for data with a regular grid structure. Images are the standard example. Pixels have fixed positions, and a small filter can slide across the image using the same operation at each location. That makes CNNs well suited to computer vision tasks where local spatial patterns matter.
• A graph neural network is designed for arbitrary topology. Nodes don’t sit in a fixed grid. They can have different numbers of neighbors, different edge types and irregular connection patterns. A GNN therefore needs aggregation functions that can operate over variable-size neighborhoods.

The two model families are related approaches to structured data. CNNs aggregate over local regions in a grid. GNNs aggregate over local neighborhoods in a graph.

The best choice depends on the data structure. If the input is naturally grid-like, such as an image, audio spectrogram or dense spatial array, a CNN may be the better fit. If the input is a network of entities and relationships, a GNN is often more appropriate. Hybrid approaches also exist, especially for data types such as point clouds, physical simulations and multimodal systems where both spatial and relational structure matter.

Graph neural networks are powerful, but they aren’t automatically the right choice whenever data can be drawn as a graph.

Many enterprise problems can be represented as graphs. Customers connect to products. Employees connect to departments. Devices connect to accounts. Transactions connect to merchants. But that doesn’t mean a GNN will outperform a simpler model.

A GNN is most likely to help when the graph structure contains information that isn’t already captured in ordinary features. If the important signal is already present in tabular form, gradient-boosted decision trees, logistic regression or other tabular methods will likely be easier to train, explain, monitor and deploy.

There are also practical limitations:

• Graph construction is hard: The model depends on which entities become nodes, which relationships become edges, how edge types are defined and how noisy or incomplete those relationships are. Poor graph design can encode weak assumptions, amplify bad data or introduce leakage.
• Scalability can also be difficult: Large graphs require careful sampling, partitioning, feature management and update strategies. A model that performs well in research may be difficult to serve when the graph changes continuously.
• Interpretability is a constraint: It can be hard to explain why a GNN made a particular prediction, especially when that prediction depends on multi-hop neighborhoods or learned attention weights. For regulated use cases, that matters.

Before a team can train a useful GNN, it needs reliable entities, relationship data, features, labels and governance. In enterprise settings, those inputs usually come from many systems: customer databases, transaction platforms, CRM tools, supply chain systems, application logs, support records, identity systems and third-party data.

Snowflake can support the data foundation around GNN workflows by keeping those inputs close to governed enterprise data. Entity tables, relationship tables, feature pipelines and training data can be prepared, managed and shared in a common environment before they’re used for graph representation learning.

Snowflake ML can help teams move from data preparation toward production machine learning workflows. Data scientists and ML engineers can build and manage ML workflows where their governed data already lives, with support for feature management, model development, model registry and serving, batch and real-time inference, observability and role-based access control. For GNN workflows, this can be useful for managing graph-derived features, maintaining repeatable training data sets, tracking models and supporting production inference patterns.

For teams training graph models with specialized libraries, Snowflake Container Runtime for ML provides preconfigured, customizable environments for machine learning on Snowpark Container Services. These environments are designed for workloads such as interactive experimentation, model training, hyperparameter tuning, batch inference and fine-tuning. They include common machine learning and deep learning frameworks and can be used with Snowflake Notebooks to support end-to-end ML development.

The graph model itself may be trained in a specialized machine learning framework, but the surrounding workflow still depends on data preparation, access control, lineage, repeatability and monitoring. The more sensitive the use case, the more important that foundation becomes.

Some predictions look entity-specific on the surface. But the real signal often comes from the system around them. Graph neural networks are powerful in these settings because they let models learn from both the entity itself and the relationships that shape its behavior.

Through message passing, a GNN updates each node representation with information from its neighborhood. Through stacked layers, it can learn from increasingly broad graph context. Through variants such as GCNs, GATs and GraphSAGE, it can adapt that basic idea to different requirements for weighting, scalability and representation learning.

The trade-off is complexity. GNNs require careful graph construction, thoughtful feature design, enough relational signal to justify the approach and an operational workflow that can handle changing graph data. Used in the wrong setting, they can be harder to manage than simpler tabular models. But used in the right setting, they make visible the structure that other models flatten away.