Graph Neural Networks (GNNs) Market Report: Size, Share, Opportunities, and Trends Segmented By Architecture, Application, End User, and Geography – Forecast 2023 to 2030

Graph Neural Networks (GNNs) Market Size:

The Graph Neural Networks (GNNs) Market is predicted to witness steady growth during the projected period.

A graph neural network (GNN) is a type of neural network that utilizes deep learning and other advanced technologies to work on designing structured data graphs, which consist of edges and nodes for representing the connection between entities. There is a rise in its ability to offer a graph database for storing valuable data, along with the ability to train predictive models. There is a rise in research and development of utilization of these models in diverse applications such as cybersecurity, genomics, computer graphics, and material science. They are used in industries for transforming the generated graphs into an improved prediction by interpreting the patterns and relations in diverse data points. Moreover, the market is expected to grow at a steady pace, driven by the widespread application in diverse industries, the growing advancement in AI and deep learning, and its integration in GNN, along with the requirement for structured data, which is promoting the market growth.

Graph Neural Networks (GNNs) Market Overview & Scope

The Graph Neural Networks (GNNs) Market is segmented by:

• Type of GNN Architecture: The graph attention network (GAT) segment is predicted to be the fastest-growing segment in the market due to a rise in demand for the production of large graphs with scalability and adaptability with a heterophily graph framework, which can be used in applications like recommendation systems and prediction, along with bioinformatics. Further, the graph convolution network is expected to hold a major market share driven by its established product with diverse applications in the production of different classified graphs, and also supported by dataset libraries for GNN advancement and growing advancement for widespread applicability of the architecture.
• Application: The Drug discovery and molecular prediction segment is expected to grow at a substantial rate in the application segment due to its application in biomedical applications for working on the development of molecular structures, which is used in drug design in cancer research, like brain tumors, and in understanding and predicting protein interactions. The growing advancement and investment in healthcare development, with the rise in personalized medicine, and for modeling biological network analysis, which is also boosting the market demand.
• End User: The finance and insurance segment is projected to hold a major share in the end-user in the graph neural network market due to the segment's reliance on graph neural networks to prevent the high-risk environment such as fraud detection understanding the credit scoring of consumers along with supporting insight of customers, these are promoting the demand for model complex which offer detection against anomalies while preventing theft and increasing the real time analysis of the segment.
• Region: The North America region is anticipated to hold the largest segment in the graph neural network market. This is due to investment in research and development related to a strong AI ecosystem and presence of major players such as Amazon and Alphabet Inc, which also supports the utilization of GNN. The growing emphasis on scalable GNN architecture is prompted by the increasing real-time data processing ability, increasing its application in diverse applications of the region.

Top Trends Shaping the Graph Neural Networks (GNNs) Market

• 1. Advancements in GNN Architectures
  • The growing trend of improvement in the performance of the industrial applications with the adaptability of diverse GNN architectures is enhancing their advancement. The GNN architecture works is handling large-scale and complex graphs in diverse applications, such as social networks and protein interaction modelling, which boosts the market.
• 2. Focus on the Explainability of the Model
  • The increasing trend in research and advancements in GNN models for prioritizing the system techniques on exposition and interpretation of its prediction in real-world applications. This is being regulated to increase the trust and adoption of GNN in diverse industries, including in sensitive sectors like finance and healthcare.

Graph Neural Networks (GNNs) Market Drivers vs. Challenges

Drivers:

• Growth in Demand for Structured Data Analysis Across Industries: the GNN works in increased analyses and modelling of large complex data, such as optimization in grid-like data structure, and leverages the graph structure utilization in capturing the dependency on diverse entities and their connected edges. In addition to this, they are widely in demand due to an increase in structured and interconnected data produced by diverse industries, which fuels the requirement for the GNN, the growing ability of processing non-Euclidean data into proper prediction and analysis, which makes GNN rise in demand for applications across industries such as e-commerce, finance, and healthcare. Additionally, the policies supporting AI innovation and integration in diverse sectors are also boosting the market, such as Society 5.0 initiatives by Japan, which is related to deepening engagement with diverse societal challenges which while supporting diverse industries like healthcare, smart city, and the finance sector to boost their productivity and utilize advanced technologies like AI in these industries.
• Increasing Application in Diverse Domains: The GNN model is utilized in diverse domain applications, such as in cancer research for supporting tumor-related drug discovery, along with biomedical sectors. The rise in focus for the development of advanced personalized medicines for rare diseases, with the analysis of genomic data, is also supporting the adoption of GNN. For instace, the United Kingdom 100,000 Genomes Project is also promoting the adoption of GNN due to its application in supporting the development of cancer research and bioinformatics. The GNNs are utilized in inherently graph-based data to provide predictions in diverse and complex biological networks, such as gene networks, molecular structures, and protein-protein interactions.

Challenges:

• The Concern of Scalability: The data loading and storage, followed by computing, is a major function of GNN, and with the rise in a wide amount of data production, could challenge the GNN in real-time scalable applications. Additionally, the large-scale graphs and millions of nodes and edges present in social networks become computation-intensive in handling for GNNs. The research is working towards handling large data sampling GNN techniques to address the scalability issue, but the system is still in development, which could hamper the global acceptance of GNN.
• Interpretability Issue: the lack of transparency in GNN leads to difficulty in understanding the prediction and analysis of the system in real-world applications, which causes the interpretability challenge. For instance, in the healthcare sector, the GNNs are being utilized for supporting cancer research for their ability to outperform non-graph methodology; however, the issue with offering no clear explanation on their result could affect the development, in turn posing a significant hurdle in the expansion of the market.

Graph Neural Networks (GNNs) Market Regional Analysis

• China: The growing AI-related initiatives supported by the government are promoting the GNN adoption in the country’s transportation and healthcare sectors. Additionally, large-scale data production in the county due to massive utilization of social media, transportation, and the e-commerce sector is also promoting the use of GNNs for offering fraud detection and analyzing the flow of traffic.
• Germany: the country's stronghold in industrial automation, with the manufacturing and automotive industry, is expected to provide an opportunity for GNN in the application for optimization of the supply chain and supporting the Industry 4.0 initiatives by providing an autonomous network system.

Graph Neural Networks (GNNs) Market Competitive Landscape

The market is fragmented, with many notable players, including Amazon, Alibaba, DeepMind (Alphabet Inc), NVIDIA Corporation, Syntiant, and IBM, among others.

• Deep Graph Library(DGL): It is a graph deep learning library for Python that works with any framework, provided by Amazon. It provides efficient and scalable message-passing primitives that operate on CPU or GPU, and integrates with PyTorch, TensorFlow, or MXNet. DGL captures the computational patterns of GNNs and expresses them as sparse tensor operations, which enables parallelization and leads to much faster speed and memory efficiency than the baselines. It also supports multi-GPU and distributed training, which allows training on graphs with billions of nodes.
• Aligraph: AliGraph is Alibaba Cloud's GNN platform, designed to serve extremely large, heterogeneous graph datasets. It operates with a layered architecture—data, engine, and application—that processes storage, sampling, and operator abstraction, simplifying the GNN development process. Its engine supports constructing colossal heterogeneous graphs (billions of nodes, trillions of edges) within minutes, and also supports millisecond-level sampling and high availability through optimized RPC, caching, and lock-free threading.

Graph Neural Networks (GNNs) Market Segmentation:

• By Type of GNN Architecture
  • Graph Convolutional Networks (GCN)
  • Spatial and Spectral-based GNNs
  • Graph Recurrent Networks (GRN)
  • Graph Attention Networks (GAT)
• By Application
  • Fraud Detection and Risk Assessment
  • Traffic flow prediction & Analysis
  • Drug Discovery and Molecular Prediction
  • Natural Language Processing
  • Computer Vision
  • Others
• By End-User
  • E-Commerce & Retail
  • Healthcare
  • Finance and Insurance
  • Transportation
  • Manufacturing
  • Others
• By Geography
  • North America
    • United States
    • Canada
    • Mexico
  • South America
    • Brazil
    • Argentina
    • Others
  • Europe
    • United Kingdom
    • Germany
    • France
    • Italy
    • Others
  • Middle East and Africa
    • Saudi Arabia
    • UAE
    • Others
  • Asia Pacific
    • Japan
    • China
    • India
    • South Korea
    • Taiwan
    • Others

Amazon

Alibaba

DeepMind (Alphabet Inc)

NVIDIA Corporation

Syntiant

IBM