HeteroHBA: A Generative Structure-Manipulating Backdoor Attack on Heterogeneous Graphs

Heterogeneous graph neural networks (HGNNs) have achieved strong performance in many real-world applications, yet targeted backdoor poisoning on heterogeneous graphs remains less studied. We consider backdoor attacks for heterogeneous node classification, where an adversary injects a small set of trigger nodes and connections during training to force specific victim nodes to be misclassified into an attacker-chosen label at test time while preserving clean performance. We propose HeteroHBA, a generative backdoor framework that selects influential auxiliary neighbors for trigger attachment via saliency-based screening and synthesizes diverse trigger features and connection patterns to better match the local heterogeneous context. To improve stealthiness, we combine Adaptive Instance Normalization (AdaIN) with a Maximum Mean Discrepancy (MMD) loss to align the trigger feature distribution with benign statistics, thereby reducing detectability, and we optimize the attack with a bilevel objective that jointly promotes attack success and maintains clean accuracy. Experiments on multiple real-world heterogeneous graphs with representative HGNN architectures show that HeteroHBA consistently achieves higher attack success than prior backdoor baselines with comparable or smaller impact on clean accuracy; moreover, the attack remains effective under our heterogeneity-aware structural defense, CSD. These results highlight practical backdoor risks in heterogeneous graph learning and motivate the development of stronger defenses.

Fractional Spike Differential Equations Neural Network with Efficient Adjoint Parameters Training

Spiking Neural Networks (SNNs) draw inspiration from biological neurons to create realistic models for brain-like computation, demonstrating effectiveness in processing temporal information with energy efficiency and biological realism. Most existing SNNs assume a single time constant for neuronal membrane voltage dynamics, modeled by first-order ordinary differential equations (ODEs) with Markovian characteristics. Consequently, the voltage state at any time depends solely on its immediate past value, potentially limiting network expressiveness. Real neurons, however, exhibit complex dynamics influenced by long-term correlations and fractal dendritic structures, suggesting non-Markovian behavior. Motivated by this, we propose the Fractional SPIKE Differential Equation neural network (fspikeDE), which captures long-term dependencies in membrane voltage and spike trains through fractional-order dynamics. These fractional dynamics enable more expressive temporal patterns beyond the capability of integer-order models. For efficient training of fspikeDE, we introduce a gradient descent algorithm that optimizes parameters by solving an augmented fractional-order ODE (FDE) backward in time using adjoint sensitivity methods. Extensive experiments on diverse image and graph datasets demonstrate that fspikeDE consistently outperforms traditional SNNs, achieving superior accuracy, comparable energy efficiency, reduced training memory usage, and enhanced robustness against noise. Our approach provides a novel open-sourced computational toolbox for fractional-order SNNs, widely applicable to various real-world tasks.