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Found 8 papers, 5 papers with code
Graph Fairness Learning under Distribution Shifts
Recently, there has been an increasing interest in ensuring fairness on GNNs, but all of them are under the assumption that the training and testing data are under the same distribution, i. e., training data and testing data are from the same graph.
Graph Contrastive Invariant Learning from the Causal Perspective
How can we fix it and encourage the current GCL to learn better invariant representations?
Directed Acyclic Graph Structure Learning from Dynamic Graphs
In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features.
Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure
However, by presenting a graph classification investigation on the training graphs with severe bias, surprisingly, we discover that GNNs always tend to explore the spurious correlations to make decision, even if the causal correlation always exists.
Debiased Graph Neural Networks with Agnostic Label Selection Bias
Then to remove the bias in GNN estimation, we propose a novel Debiased Graph Neural Networks (DGNN) with a differentiated decorrelation regularizer.
Generalizing Graph Neural Networks on Out-Of-Distribution Graphs
Graph Neural Networks (GNNs) are proposed without considering the agnostic distribution shifts between training and testing graphs, inducing the degeneration of the generalization ability of GNNs on Out-Of-Distribution (OOD) settings.
A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources
Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.
Decorrelated Clustering with Data Selection Bias
Most of existing clustering algorithms are proposed without considering the selection bias in data.
