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Out-of-Distribution Generalized Dynamic Graph Neural Network with Disentangled Intervention and Invariance Promotion
March 11, 2024, 4:42 a.m. | Zeyang Zhang, Xin Wang, Ziwei Zhang, Haoyang Li, Wenwu Zhu
cs.LG updates on arXiv.org arxiv.org
Abstract: Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to handle distribution shifts, which naturally exist in dynamic graphs, mainly because the patterns exploited by DyGNNs may be variant with respect to labels under distribution shifts. In this paper, we propose Disentangled Intervention-based Dynamic graph Attention networks with Invariance Promotion (I-DIDA) to handle spatio-temporal distribution shifts in dynamic graphs by discovering and …
abstract arxiv cs.lg distribution dynamic dynamics generalized graph graph neural network graph neural networks graphs however network networks neural network neural networks patterns predictive promotion temporal type
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