A Lightweight Deep Learning-based Model for Ranking Influential Nodes in Complex Networks

Researchers introduce 1D-CGS, a lightweight deep learning model combining 1D-CNN and GraphSAGE for identifying influential nodes in complex networks. The model achieves 4.73% improvement over existing methods while maintaining significantly faster computational performance, with applications across network analysis domains.

The identification of influential nodes in complex networks represents a fundamental challenge with practical applications spanning social networks, epidemiology, and infrastructure systems. The 1D-CGS model addresses a persistent tension in network science: existing methods either sacrifice accuracy for speed or require substantial computational resources. By leveraging convolutional neural networks for pattern extraction and GraphSAGE for neighborhood aggregation, the researchers create a hybrid architecture that maintains competitive accuracy while dramatically reducing runtime.

The approach builds on established deep learning techniques adapted specifically for network topology. Rather than relying on expensive graph neural network computations alone, the model uses minimal topological features—node degree and average neighbor degree—as input, reducing preprocessing overhead. This design choice reflects a broader trend in machine learning toward efficient, interpretable models that don't require exhaustive feature engineering. The use of the SIR epidemiological model for generating ground truth influence scores provides a theoretically grounded benchmark, distinguishing this work from purely empirical approaches.

For practitioners working with large-scale networks, the performance metrics matter considerably. A 4.73% improvement in Kendall's Tau correlation translates to meaningfully more accurate node rankings, while near-perfect Monotonicity Index scores indicate the model produces highly discriminative results. The computational efficiency gains position this approach for real-time applications in dynamic network analysis, where traditional deep learning methods would create bottlenecks.

Future developments should explore how 1D-CGS performs on heterogeneous networks and temporal dynamics. The validation across twelve real-world datasets demonstrates generalizability, though expanding to domain-specific networks could reveal optimization opportunities specific to social, biological, or technological systems.

• →1D-CGS achieves 4.73% accuracy improvement over deep learning baselines while running significantly faster than existing methods
• →The hybrid model uses only node degree and average neighbor degree as input features, minimizing preprocessing complexity
• →Monotonicity Index score of 0.99 indicates the model produces highly unique and discriminative node rankings
• →Training on synthetic Barabasi-Albert networks and testing on real-world networks demonstrates strong generalization capability
• →Computational efficiency makes the approach viable for large-scale network applications requiring real-time node importance assessment