Graph Neural Networks & Transformers: Same Song, Different Venues

mahdinaser
Sep 7
4 min read

Graph Neural Networks and Transformers: Parallels and Differences

Introduction

In recent years, two model families have defined much of the progress in machine learning: Graph Neural Networks (GNNs) and Transformers. While they were designed for very different data structures—graphs and sequences—they share common ideas in how they process and integrate information.

Both models are essential to today’s AI landscape. Transformers power large language models and breakthroughs in natural language processing, while GNNs are becoming the go-to architecture for reasoning over complex relational data such as molecules, social networks, or recommendation systems.

This article explores the similarities and differences between GNNs and Transformers, highlighting their unique strengths, applications, and the possibility of hybrid approaches in the future.

Background of GNNs and Transformers

Graph Neural Networks (GNNs)

GNNs are designed to learn directly from graph-structured data, where entities (nodes) are connected by relationships (edges). Instead of treating each data point as independent, GNNs pass messages along edges to update node representations.

Definition: A GNN learns node and graph-level representations by iteratively aggregating information from neighbors.
Historical context: Early work on GNNs appeared in the mid-2000s, but the field gained momentum after the introduction of Graph Convolutional Networks (GCN) in 2016 and Graph Attention Networks (GAT) shortly after. These innovations brought scalability and flexibility, leading to adoption across chemistry, social sciences, and recommendation systems.

Transformers

Transformers, introduced in 2017 with the paper “Attention is All You Need”, redefined how sequential data can be processed. Unlike recurrent models, which rely on step-by-step computation, Transformers use self-attention to model relationships between all tokens in parallel.

Definition: A Transformer computes contextual embeddings of tokens by comparing each token with all others through attention scores.
Impact: This architecture became the foundation of modern large language models (e.g., GPT, BERT, T5) and has expanded beyond text to images, audio, and even reinforcement learning.

Similarities Between GNNs and Transformers

Although GNNs and Transformers evolved in different research communities, they share striking similarities:

Attention as the core mechanism
- GNNs (e.g., GAT) use attention to decide which neighbors are most relevant.
- Transformers use self-attention to weight the importance of tokens in a sequence.
Parallelization and scalabilityBoth models move beyond sequential or handcrafted features, allowing parallel updates across nodes or tokens, making training more efficient on modern hardware.
Broad applicability
- GNNs shine in domains like chemistry, social networks, and recommendation engines.
- Transformers dominate natural language processing but are also used in vision, biology, and multimodal learning.

Differences Between GNNs and Transformers

Despite their shared principles, the differences are just as important:

Data structures
- GNNs are inherently tied to graphs, using adjacency information to constrain interactions.
- Transformers assume a sequence (or fully connected graph) and rely on positional encodings to preserve order.
Information flow
- In GNNs, nodes aggregate information from local neighborhoods. Depth defines how far information travels.
- In Transformers, every token can directly attend to every other token in a layer, enabling global communication from the start.
Representation focus
- GNNs emphasize node-level and graph-level representations.
- Transformers emphasize sequence-level representations (e.g., via a [CLS] token) or contextual embeddings.

Practical Applications