Graph neural networks: an efficient energy solution for structured data analysis
01/29/2025
With the rise of artificial intelligence models, the question of energy consumption by algorithms has become central. Transformer models, although extremely powerful, are often criticized for their high energy costs, particularly when processing complex data. By contrast, Graph Neural Networks(GNNs) are emerging as a much more energy-efficient alternative for certain applications, notably the analysis of structured data and the detection of network effects. This article explores why and how GNNs outperform Transformers in terms of energy efficiency.
Transformers’ energy challenges
Transformers, introduced in 2017 with the seminal article “Attention is All You Need”have revolutionized sequence processing in fields such as natural language processing (NLP) and computer vision. However, their architecture relies on an auto-regressive attention mechanism that compares each element of a sequence with all the others. This approach requires exponential computing power in relation to the size of the data.
High energy consumption is due to three main factors:
1/ Model size: Transformers like GPT-4 contain billions of parameters, requiring a massive, power-hungry GPU infrastructure.
2/ Computational complexity: The O( n2) complexity of attention mechanisms, where n is the length of the sequence, is an energy sink for large graphs or sequences.
3/ Training : Training a Transformer model can consume thousands of kilowatt-hours (kWh), generating significant CO₂ emissions.
GNN: economical, specialized architecture
Graph neural networks are specifically designed to process structured data, such as social networks, molecules or infrastructure networks. Unlike Transformers, GNNs exploit the intrinsic structure of graphs to make predictions by focusing solely on local relationships.
Why do GNNs consume less energy?
Local optimization :
- GNNs use local aggregation mechanisms, often referred to as message passing. Each node exchanges information only with its immediate neighbors, reducing the need for global calculations.
- This approach has a linear complexity as a function of the number of nodes and edges, i.e. O(∣V∣+∣E∣), far more economical than Transformers.
Parameter parsimony :
- GNN models are generally much smaller than Transformers, with millions rather than billions of parameters.
- Fewer parameters mean lower memory and computing power requirements.
Adaptation to structured data :
- Where Transformers must convert graphs into sequences (resulting in computational overhead), GNNs process graphs directly, eliminating costly pre-processing steps.
Comparative studies of energy consumption
D. Trystman, R. Couillet and T. Ménissier call for an AI ethic for greater energy sobriety in a joint article:“Deep learning and energy consumption: the submerged part of the AI-ceberg?
Indeed, recent research has highlighted the energy difference between GNN and Transformers on graph tasks. For example:
- Detecting communities in social networks :
- A GCN (Graph Convolutional Network) model has processed graphs containing millions of nodes with an energy consumption 10 to 20 times lower than that of a modified Transformer for graphs (such as Graph-BERT).
- Prediction of molecular properties :
- GNNs, like the Message Passing Neural Network (MPNN) model, consume around 5 times less energy than a Transformer for similar tasks, while achieving comparable performance.
- Large-scale models :
- On very large-scale graphs, such as those for road infrastructures or power grids, GNNs have shown up to 30 times lower energy consumption, not least because they require fewer passes to converge.
Typical GNN and Transformer applications
| Tasks | Transformers models | Neural networks in graphs |
|---|---|---|
| NLP (translation, summary) | GPT, BERT | Unsuitable |
| Social network analysis | Graph-BERT, Graphormer | GCN, GAT, MPNN |
| Molecular prediction | MolBERT | Message Passing Networks |
| Anomaly detection | Transformers applied to graphs | Specialized GNN |
| Recommendation systems | Multimodal transformers | Graph-based collaborative filtering |
A future focused on energy efficiency
As environmental concerns grow, GNNs offer a viable alternative for graph-intensive tasks. Their reduced energy consumption is a major asset in critical areas such as :
- Scientific research, where massive graphs (genomics, chemistry) are commonplace.
- Technological infrastructures, where the analysis of flows and networks requires an efficient approach.
- Recommendation systems, where user-product graphs are constantly evolving.
Conclusion
The energy consumption of graph neural networks is therefore much lower than that of Transformers.
Transformers are powerful models, but their high energy consumption makes them inefficient for certain specific tasks. Graph neural networks (GNNs), with their targeted, energy-efficient processing, offer a more sustainable alternative, while meeting the requirements of structured data applications.
In this context, solutions like those developed by Heptalytics, which harness the power of GNNs to analyze complex networks and detect relational effects, stand out for their energy efficiency and accuracy. By taking advantage of the natural structures of graphs, these technologies deliver optimal performance while reducing the carbon footprint of AI.
As demand forgreen AI grows, GNNs and players like Heptalytics are emerging as the solutions of choice for combining analytical power and energy efficiency.
