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Multi-structural view knowledge distillation for node influence prediction in complex networks
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DOI：10.1016/j.eswa.2025.129280.png)
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Abstract
En 中文
Identifying influential nodes in complex networks is a fundamental problem with important applications in viral marketing, epidemic control, and social influence analysis. A key challenge in this domain is the scarcity of ground-truth labels: computing accurate influence scores typically requires repeated simulations using diffusion models, which is computationally expensive on large-scale graphs. Additionally, existing deep learning models, particularly those based on graph neural networks (GNNs), often incur high inference costs, limiting their practical deployment. To address these challenges, we propose DistillRWGCN, a knowledge distillation framework based on multi-structural views that captures both structural and local topological features for influence prediction. The framework integrates global and local views of the graph using breadth-first search and depth-first search strategies, fuses the resulting embeddings through an attention mechanism, and distills this knowledge into a lightweight TinyGCN student model via contrastive alignment. DistillRWGCN is trained using only a small subset of influence scores and employs a novel hybrid loss that combines pairwise ranking and Spearman correlation to enhance both prediction accuracy and ranking consistency. Extensive experiments on nine real-world datasets demonstrate that DistillRWGCN achieves up to 23.7 % lower error and 12.5 % higher ranking correlation than state-of-the-art baselines, while significantly reducing inference time. These results highlight the mode’s effectiveness in label-scarce and resource-constrained scenarios.
Keywords:
Graph representation learning
Knowledge distillation
Node influence prediction
Multi-view learning
Contrastive learning
Node ranking
Journal
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7.5
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2.9W
Citations:
10.2W