Large Language Models vs Graph Neural Networks: It Depends

Most of the AI discussion and headlines over the last year has been dominated by Large Language Models. While LLMs like GPT-4, Claude, and Gemini have captured headlines and human imagination with their conversational abilities, graph-based models quietly power critical systems from fraud detection to drug discovery. Understanding when to deploy each approach can mean the difference between breakthrough performance and costly inefficiency. At Symmetry Systems, we believe in a best-of-breed approach to AI implementation, carefully matching the right technology to each specific use case rather than defaulting to whatever solution dominates the current news cycle. 

Our philosophy centers on understanding that no single AI architecture excels at everything—LLMs shine for natural language tasks and reasoning, while graph neural networks excel at relationship modeling and pattern detection in complex networks. By maintaining expertise across multiple AI paradigms and rigorously evaluating each use case’s requirements, we ensure our solutions deliver optimal performance rather than just following the latest trends. Understanding when to deploy each approach can mean the difference between breakthrough performance and costly inefficiency.

#TLDR 

• Graph Neural Networks (GNNs): Best for structured, interconnected data like social networks, fraud detection, and drug discovery. Great for real-time inference and interpretable results.
• Large Language Models (LLMs): Best for natural language tasks, reasoning, and few-shot learning on unstructured text. Ideal for tasks needing contextual understanding across long sequences.
• Choosing the right one depends on your data structure, performance needs, and interpretability requirements.

How These Models See the World Differently

At their core, these two paradigms represent fundamentally different approaches to information processing and representation. Graph Neural Networks operate on discrete structures where entities (nodes) are connected by explicit relationships (edges), enabling direct reasoning over network topology and multi-hop connections. Large Language Models, in contrast, process sequential token streams, using attention mechanisms to capture dependencies and transformer architectures to predict probability distributions over vocabulary based on contextual patterns learned from vast text training datasets.

A Real World Explainer

In the real world, these differences are easily explained. Think about how you’d draw your social network to show to a friend. You’d probably draw connections between various people and objects: 

You’re thinking in terms of relationships, networks, webs of connection. You’d be thinking in terms of spatial connections and structural relationships — much like how graph neural networks operate, and how we visualize our data and identity relationships at Symmetry.

Now think about how you’d tell that picture story in a text message. You’d write it out sequentially: “I have never met Lisa until my wife’s college reunion last week. Lisa is dating Mike. Mike went to college with my wife, Sarah almost 20 years ago. Sarah and I used to work together at KPMG.” You’re thinking in terms of sequences, context, the flow of language.

You’re creating a description using a set of sequential words and thoughts – building up an image through a series of attributes and descriptors. What’s happening under the hood when an LLM processes or generates this description is fundamentally about probability: given the sequence “Sarah and I used to,” what’s the most likely next word or phrase based on patterns learned from millions of similar sequences? The model calculates the probability that “work together” follows “<Person> and I used to” more often than “catch purple tentacles” in real-world text.

That’s essentially the difference between how graph neural networks and large language models understand the world. GNNs excel at this relational thinking — they see entities and relationships, nodes and edges, the rich interconnected structure of data. LLMs excel at sequential understanding — they process sequences, context, and most importantly, the statistical patterns and probabilities that govern how words and concepts follow each other in human communication.

Neither approach is inherently better. They’re just different ways of making sense of information, optimized for different types of reasoning.

Application to Data Security and Information Governance

This fundamental difference mirrors what we see in information flow control systems. Consider how permissions work in enterprise environments: users connect to systems, systems connect to data stores, roles define access paths between identities and resources. 

When you’re trying to understand “Can User A access Database C through any roles or groups?” – it would be a significant amount of processing to take the sequence of words describing each user and permission to calculate all the probabilities of word combinations that describe the roles and group members to determine whether they have access. A linear narrative, especially at enterprise scale, is resource intensive compared to the network of connections and  the permission chains on a graph. You’re reasoning about a graph where nodes represent identities, systems, and data stores, while edges represent permissions, trust relationships, and access controls. The question becomes a path-finding problem through a network of relationships. 

Similarly when you’re trying to understand “Is this document worth protecting?” – you can use all the sequence of words in a document to determine the sensitivity – and this could allow you to  make meaningful decisions on what top of document it is, and whether it is worth protecting based on all the content within a document. In this case, the resource intensiveness processing of LLM’s may be worth it, but other less intensive processes to find identifiers within the document in combination with a graph can deliver similar results, but requires knowledge of what identifiers to look for.

The Fundamental Architectural Differences

Use Case Deep Dive: Which Model Wins Where?

Knowledge Graph Reasoning

GNNs dominate when it comes to structured reasoning, like knowledge graph completion or entity relationship mapping. Recent advances in GAT-based approaches show significant improvements, with models achieving 5.2% better performance on standard benchmarks (Wei et al., 2024). They offer better accuracy, faster inference, and full explainability. LLMs can be competitive via prompt engineering but struggle with multi-hop reasoning and consistency.

Fraud Detection & Anomaly Detection

Fraud rings and transaction anomalies are inherently relational problems. GNNs model these networks efficiently, offering sub-10ms inference and explainable reasoning paths (NVIDIA, 2024; Hu et al., 2024). The models excel at detecting suspicious patterns that only emerge when examining connections between entities — fake accounts interacting with each other, money flowing through chains of accomplices.

LLMs face significant challenges here: they’re slow, opaque, and require expensive text conversions. Converting financial transactions to natural language also risks losing critical relational information that graph models capture naturally.

Natural Language Processing & Text Generation

Here, LLMs reign supreme. They outperform graph models on tasks like question answering, translation, and summarization by significant margins, as demonstrated in comprehensive benchmarks like SuperGLUE (Wang et al., 2019). Their ability to generalize from few examples and handle long-range dependencies makes them the natural choice for language tasks. The unstructured, contextual nature of human communication is exactly what these models were designed to handle.

Recommendation Systems

GNN-based recommenders like LightGCN currently offer superior accuracy and explainability by modeling user-item interactions as networks (He et al., 2020). They can capture complex collaborative filtering patterns and provide clear reasoning for recommendations. Web-scale implementations have demonstrated significant performance improvements over traditional methods (Ying et al., 2018). LLMs are showing promise, especially for natural language recommendations and personalization, but pure accuracy still favors graph approaches in most scenarios.

Computer Vision & Image Understanding

This domain sees no clear winner. GNNs shine in tasks like scene graphs and 3D object modeling where spatial relationships matter. They excel at understanding how objects relate to each other in physical space. LLMs (or more accurately, multimodal models like GPT-4V) excel at combining image and text understanding, but typically need specialized extensions for visual tasks.

Computational Trade-Offs

Understanding the operational differences between these approaches is crucial for practical deployment:

These differences matter significantly in production environments. Graph models’ speed and efficiency advantages make them particularly attractive for real-time applications, while LLMs’ computational requirements limit their use in resource-constrained scenarios.

Decision Framework: When to Choose Which

Use GNNs when:

• Your data has explicit relationships (networks, graphs, molecules)
• Real-time or low-latency inference is required
• Interpretability and explainability matter
• You’re operating under resource constraints
• The problem involves reasoning over structural patterns

Use LLMs when:

• You’re working with unstructured text
• You need reasoning and few-shot learning capabilities
• Versatility across different task types is important
• Human-like language generation is needed
• The problem requires understanding context and nuance

The Hybrid Future

The most powerful systems like Symmetry Systems increasingly blend both approaches rather than choosing sides – as evidenced by recent advances in graph neural network architectures and evaluation frameworks (Dwivedi et al., 2020; Fey & Lenssen, 2019):

• Graph-Enhanced LLMs inject structured graph reasoning into language models, allowing them to maintain consistency across relational facts while retaining their language capabilities.
• LLM-Powered Graph Construction uses language models to extract entity relationships from unstructured text, automatically building knowledge graphs that can then be processed by GNNs.
• Multi-Modal AI Systems pair graph reasoning with natural language interfaces, providing both the accuracy of structured reasoning and the accessibility of conversational interaction.

These hybrid approaches represent the cutting edge of AI system design, leveraging the complementary strengths of both paradigms.

Closing Thoughts

The best AI deployments aren’t about picking a side in some imaginary battle between graph neural networks and large language models. They’re about choosing the right tool for the job based on data structure, performance requirements, and business constraints.

Understanding both paradigms deeply — their strengths, limitations, and ideal use cases — enables building systems that are not only performant but also interpretable, scalable, and cost-effective. In a field dominated by hype cycles and one-size-fits-all solutions, this nuanced approach represents good engineering judgment.

In AI, as in all engineering disciplines, “it depends” isn’t a cop-out. It’s the mark of thoughtful technical decision-making.