LoReTTA: A Low Resource Framework To Poison Continuous Time Dynamic Graphs
Himanshu Pal, Venkata Sai Pranav Bachina, Ankit Gangwal, Charu Sharma

TL;DR
LoReTTA is a novel low-resource adversarial framework that significantly degrades the performance of continuous-time dynamic graph neural networks across multiple datasets and models, highlighting security vulnerabilities.
Contribution
Introduces LoReTTA, a two-phase attack method that effectively poisons temporal graph neural networks without expensive surrogate models or detection, advancing security research in dynamic graphs.
Findings
Degrades TGNN performance by up to 42% on key datasets.
Outperforms 11 baseline attack methods.
Remains undetectable and robust against several defenses.
Abstract
Temporal Graph Neural Networks (TGNNs) are increasingly used in high-stakes domains, such as financial forecasting, recommendation systems, and fraud detection. However, their susceptibility to poisoning attacks poses a critical security risk. We introduce LoReTTA (Low Resource Two-phase Temporal Attack), a novel adversarial framework on Continuous-Time Dynamic Graphs, which degrades TGNN performance by an average of 29.47% across 4 widely benchmark datasets and 4 State-of-the-Art (SotA) models. LoReTTA operates through a two-stage approach: (1) sparsify the graph by removing high-impact edges using any of the 16 tested temporal importance metrics, (2) strategically replace removed edges with adversarial negatives via LoReTTA's novel degree-preserving negative sampling algorithm. Our plug-and-play design eliminates the need for expensive surrogate models while adhering to realistic…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Adversarial Robustness in Machine Learning · Explainable Artificial Intelligence (XAI)
