Degree-Conscious Spiking Graph for Cross-Domain Adaptation

Understand

Spiking Graph Networks (SGNs) have demonstrated significant potential in graph classification by emulating brain-inspired neural dynamics to achieve energy-efficient computation.

• However, existing SGNs are generally constrained to in-distribution scenarios and struggle with distribution shifts.
• In this paper, we first propose the domain adaptation problem in SGNs, and introduce a novel framework named Degree-Consicious Spiking Graph for Cross-Domain Adaptation (DeSGraDA).
• DeSGraDA enhances generalization across domains with three key components.