Degree Distribution based Spiking Graph Networks for Domain Adaptation

• URL: http://arxiv.org/abs/2410.06883v3
• Date: Tue, 04 Feb 2025 12:23:55 GMT
• Title: Degree Distribution based Spiking Graph Networks for Domain Adaptation
• Authors: Yingxu Wang, Mengzhu Wang, Siwei Liu, Nan Yin,
• Abstract summary: Spiking Graph Networks (SGNs) have garnered significant attraction from researchers and industry due to their ability to address energy consumption challenges in graph classification. We first propose the domain adaptation problem in SGNs, and introduce a novel framework named Degree-aware Spiking Graph Domain Adaptation for Classification (DeSGDA) The proposed DeSGDA addresses the spiking graph domain adaptation problem in three aspects: node degree-aware personalized spiking representation, adversarial feature distribution alignment, and pseudo-label distillation.
• Score: 8.311835054795779
• License:
• Abstract: Spiking Graph Networks (SGNs) have garnered significant attraction from researchers and industry due to their ability to address energy consumption challenges in graph classification. However, SGNs are only effective for in-distribution data and cannot tackle out-of-distribution data. In this paper, we first propose the domain adaptation problem in SGNs, and introduce a novel framework named Degree-aware Spiking Graph Domain Adaptation for Classification (DeSGDA). The proposed DeSGDA addresses the spiking graph domain adaptation problem in three aspects: node degree-aware personalized spiking representation, adversarial feature distribution alignment, and pseudo-label distillation. First, we introduce the personalized spiking representation method for generating degree-dependent spiking signals. Specifically, the node degree determines the threshold of triggering a spike, allowing this personalized approach to capture more expressive information for classification. Then, we propose the graph feature distribution alignment module that is adversarially trained using membrane potential against a domain discriminator. Such an alignment module can efficiently maintain high performance and low energy consumption in the case of inconsistent distribution. Additionally, we extract consistent predictions across two spaces to create reliable pseudo-labels, effectively leveraging unlabeled data to enhance classification performance. Extensive experiments on benchmark datasets validate the superiority of the DeSGDA compared with competitive baselines.

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