Graph Neural Networks (GNNs) have emerged because the main method for graph studying duties throughout varied domains, together with recommender techniques, social networks, and bioinformatics. Nonetheless, GNNs have proven vulnerability to adversarial assaults, notably structural assaults that modify graph edges. These assaults pose important challenges in eventualities the place attackers have restricted entry to entity relationships. Regardless of the event of quite a few strong GNN fashions to defend towards such assaults, present approaches face substantial scalability points. These challenges stem from excessive computational complexity as a result of complicated protection mechanisms and hyper-parameter complexity, which requires intensive background information and complicates mannequin deployment in real-world eventualities. Consequently, there’s a urgent want for a GNN mannequin that achieves adversarial robustness towards structural assaults whereas sustaining simplicity and effectivity.
Researchers have overcome structural assaults in graph studying by two major approaches: growing efficient assault strategies and creating strong GNN fashions for protection. Assault methods like Mettack and BinarizedAttack use gradient-based optimization to degrade mannequin efficiency. Defensive measures embrace purifying modified constructions and designing adaptive aggregation methods, as seen in GNNGUARD. Nonetheless, these strong GNNs typically undergo from excessive computational overhead and hyper-parameter complexity. Latest efforts like NoisyGCN and EvenNet purpose for effectivity by simplifying protection mechanisms however nonetheless introduce further hyper-parameters requiring cautious tuning. Whereas these approaches have made important strides in decreasing time complexity, the problem of growing easy but strong GNN fashions persists, driving the necessity for additional innovation within the area.
Researchers from The Hong Kong Polytechnic College, The Chinese language College of Hong Kong, and Shanghai Jiao Tong College introduce SFR-GNN (Easy and Quick Sturdy Graph Neural Community), a novel two-step method to counter structural assaults in graph studying. The strategy pre-trains on node attributes, then fine-tunes on structural data, disrupting the “paired impact” of assaults. This easy technique achieves robustness with out further hyper-parameters or complicated mechanisms, considerably decreasing computational complexity. SFR-GNN’s design makes it practically as environment friendly as vanilla GCN whereas outperforming present strong fashions in simplicity and ease of implementation. By pairing manipulated constructions with pre-trained embeddings as an alternative of unique attributes, SFR-GNN successfully mitigates the affect of structural assaults on mannequin efficiency.
SFR-GNN introduces a two-stage method to counter structural assaults in graph studying: attribute pre-training and construction fine-tuning. The pre-training stage learns node embeddings solely from attributes, excluding structural data, to supply uncontaminated embeddings. The fine-tuning stage then incorporates structural data whereas mitigating assault results by distinctive contrastive studying. The mannequin employs Inter-class Node attribute augmentation (InterNAA) to generate various node options, additional decreasing the affect of contaminated structural data. By studying from much less dangerous mutual data, SFR-GNN achieves robustness with out complicated purification mechanisms. SFR-GNN’s computational complexity is akin to vanilla GCN and considerably decrease than present strong GNNs, making it each environment friendly and efficient towards structural assaults.
SFR-GNN has demonstrated outstanding efficiency in defending towards structural assaults on graph neural networks. Experiments performed on extensively used benchmarks like Cora, CiteSeer, and Pubmed, in addition to large-scale datasets ogbn-arxiv and ogbn-products, present that the proposed SFR-GNN technique constantly achieves high or second-best efficiency throughout varied perturbation ratios. As an example, on the Cora dataset underneath Mettack with a ten% perturbation ratio, SFR-GNN achieves 82.1% accuracy, outperforming baselines that vary from 69% to 81%. The strategy additionally exhibits important enhancements in coaching time, attaining over 100% speedup on Cora and Citeseer in comparison with the quickest present strategies. On large-scale graphs, SFR-GNN demonstrates superior scalability and effectivity, surpassing even GCN in pace whereas sustaining aggressive accuracy.
SFR-GNN emerges as an modern and efficient resolution for defending towards structural assaults on graph neural networks. By using an modern “attributes pre-training and construction fine-tuning” technique, SFR-GNN eliminates the necessity to purify modified constructions, considerably decreasing computational overhead and avoiding further hyper-parameters. Theoretical evaluation and intensive experiments validate the strategy’s effectiveness, demonstrating robustness akin to state-of-the-art baselines whereas attaining a outstanding 50%-136% enchancment in runtime pace. Additionally, SFR-GNN reveals superior scalability on large-scale datasets, making it notably appropriate for real-world purposes that demand each reliability and effectivity in adversarial environments. These findings place SFR-GNN as a promising development within the area of strong graph neural networks, providing a steadiness of efficiency and practicality for varied graph-based duties underneath potential structural assaults.
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