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Found 7 papers, 4 papers with code
Load Data Valuation in Multi-Energy Systems: An End-to-End Approach
Accurate load forecasting serves as the foundation for the flexible operation of multi-energy systems (MES).
Uplift Modeling based on Graph Neural Network Combined with Causal Knowledge
Through uplift modeling, we can identify the treatment with the greatest benefit.
Double Equivariance for Inductive Link Prediction for Both New Nodes and New Relation Types
We then show how double-equivariant architectures are able to self-supervise pre-train on distinct KG domains and zero-shot predict links on a new KG domain (with completely new entities and new relation types).
Bias Challenges in Counterfactual Data Augmentation
Deep learning models tend not to be out-of-distribution robust primarily due to their reliance on spurious features to solve the task.
OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs
This work provides the first theoretical study on the ability of graph Message Passing Neural Networks (gMPNNs) -- such as Graph Neural Networks (GNNs) -- to perform inductive out-of-distribution (OOD) link prediction tasks, where deployment (test) graph sizes are larger than training graphs.
Size-Invariant Graph Representations for Graph Classification Extrapolations
In general, graph representation learning methods assume that the train and test data come from the same distribution.
On Single-environment Extrapolations in Graph Classification and Regression Tasks
Extrapolation in graph classification/regression remains an underexplored area of an otherwise rapidly developing field.
