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Found 4 papers, 4 papers with code
Diffusion-based Negative Sampling on Graphs for Link Prediction
Furthermore, in the context of link prediction, most previous methods sample negative nodes from existing substructures of the graph, missing out on potentially more optimal samples in the latent space.
Link Prediction on Latent Heterogeneous Graphs
Assuming no type information is given, we define a so-called latent heterogeneous graph (LHG), which carries latent heterogeneous semantics as the node/edge types cannot be observed.
On Generalized Degree Fairness in Graph Neural Networks
In particular, the varying neighborhood structures across nodes, manifesting themselves in drastically different node degrees, give rise to the diverse behaviors of nodes and biased outcomes.
On Data Augmentation for GAN Training
We provide theoretical analysis to show that using our proposed DAG aligns with the original GAN in minimizing the Jensen-Shannon (JS) divergence between the original distribution and model distribution.
