Rethinking Message Passing as Retrieval for Text-Attributed Graph Learning
This work proposes RTA, a simple MLP-based framework that replaces structural message passing with label-aware retrieval and propagation and provides theoretical insights that connect retrieval-based aggregation to softmax-attention message passing and establish the robustness of retrieved-context supervision to mis-retrieved outliers.
Jintang Li, Yuhong Chen, Ruo-Fan Wu et al.
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