1 code implementation • 25 Mar 2024 • Trung-Kien Nguyen, Yuan Fang
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.
1 code implementation • 21 Feb 2023 • Trung-Kien Nguyen, Zemin Liu, Yuan Fang
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.
1 code implementation • 8 Feb 2023 • Zemin Liu, Trung-Kien Nguyen, Yuan Fang
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.
1 code implementation • 9 Jun 2020 • Ngoc-Trung Tran, Viet-Hung Tran, Ngoc-Bao Nguyen, Trung-Kien Nguyen, Ngai-Man Cheung
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.