International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 10 | Oct 2025
p-ISSN: 2395-0072
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Efficient Optimization Algorithms for Large-Scale Graph Neural Networks Binay Kumar Sah1, Md Sarazul Ali2, Nadim Akhtar3, Mohd Shahzad4 1
Packaged App Development Associate, Accenture, India
2Senior Associate Technical Consultant, Ahead DB, India 3Infrastructure Administration, Innovecture, India 4AWS and DevOps Engineer, Deloitte, India
---------------------------------------------------------------------***--------------------------------------------------------------------methods are crucial for training large-scale GNNs without Abstract - Graph Neural Networks (GNNs) have emerged as a powerful paradigm for learning over graph-structured data. However, as graph sizes grow to millions or billions of nodes and edges, the computational cost and memory consumption associated with training GNNs become prohibitively large. This paper investigates efficient optimization algorithms designed specifically for large-scale GNNs. We explore stochastic, distributed, and adaptive optimization strategies that address convergence speed, scalability, and model generalization. Our analysis focuses on gradient compression, sampling strategies, and asynchronous optimization, highlighting their roles in improving training efficiency. Experimental results demonstrate that hybrid optimization frameworks combining mini-batch stochastic gradient descent (SGD) with adaptive learning rate schedulers significantly reduce training time while maintaining model accuracy. Finally, we identify open research challenges and suggest directions for future work, including energy-efficient optimization and neural architecture search for large-scale graph learning.
compromising accuracy or convergence stability.
Key Words: Graph Neural Networks (GNNs); Large-Scale Optimization; Distributed Learning; Gradient Compression; Adaptive Optimizers; Scalability; Stochastic Gradient Descent (SGD); Graph Partitioning.
However, their effectiveness depends on the assumption that training samples are independent and identically distributed (i.i.d.), which is rarely the case in graph-structured data. In graphs, each node’s representation depends on its neighbors, introducing inter-dependencies and non-i.i.d. properties. Consequently, standard deep learning optimizers may struggle to achieve stable convergence on large, irregular, and sparse graph data.
This paper systematically reviews and proposes optimization strategies for GNNs that focus on scalability, distributed computation, and convergence improvement. We also implement and evaluate these methods on benchmark datasets to demonstrate their efficacy.
2. LITERATURE REVIEW / RELATED WORK 2.1 Optimization in Deep Learning Optimization lies at the heart of deep learning, determining how models learn from data and converge toward minimal loss. Classical optimization algorithms such as Stochastic Gradient Descent (SGD) and its variants — Momentum, Nesterov Accelerated Gradient (NAG), and Adam — have revolutionized neural network training. These optimizers adjust parameters iteratively to minimize loss functions by leveraging backpropagation and gradient updates.
1.INTRODUCTION Graph-structured data naturally appear in numerous domains such as social networks, biological networks, recommender systems, and knowledge graphs. Graph Neural Networks (GNNs) extend deep learning to these domains by propagating and aggregating information across nodes and edges. Despite their success, the optimization of GNNs on large-scale graphs remains a major challenge due to issues like gradient vanishing, over-smoothing, and high communication costs.
Furthermore, optimization in deep learning typically scales linearly with the number of samples, whereas in GNNs, complexity scales with both node count and edge density, causing a combinatorial increase in computation. Therefore, traditional optimizers need modification to handle graphspecific constraints, such as neighbor sampling, mini-batch aggregation, and hierarchical feature propagation.
Traditional optimization algorithms like Stochastic Gradient Descent (SGD) and Adam struggle with scalability when applied to massive graph datasets such as Reddit, OGBNProducts, or MAG240M. The interconnectedness of nodes creates dependencies that make mini-batch sampling and parallelization non-trivial. Therefore, efficient optimization
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Recent innovations, including adaptive learning rates (AdamW, LAMB) and variance reduction techniques (SAGA, SVRG), have shown promise in mitigating gradient noise and improving convergence. Nevertheless, when applied to large-scale GNNs, additional challenges such as gradient staleness and communication latency must also
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