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Coarse RNA coordinate representations are widely used, yet the biological information they encode remains unquantified. We introduce FORGE, which converts a seven-atom RNA geometry representation into 935 interpretable descriptors and reports which residue-level annotations this geometry supports. On 4,135 post-2025 RN...
The message passing mechanism, which updates node representations by exchanging messages with their neighbors, plays a critical role in graph neural networks (GNNs) for capturing structural patterns. Since the single message passing mechanism lacks the flexibility to handle graphs with differing node feature quality, G...
This work investigates the problem of graph fairness without demographic information and proposes Grad2Fair, a gradient-guided approach for group fairness without demographics that directly leverages gradients to debias and eliminates demographic prediction, thereby enabling stable fairness performance.
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-re...
This work proposes Mixture of Message Passing (MoMP), a novel mechanism that incorporates the Mixture of Experts (MoE) paradigm directly into the message passing mechanism of GNNs, treating different message passing mechanisms as ''experts''.
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