Posterior Label Smoothing for Node Classification

Recent advancements in graph neural networks (GNNs) have highlighted their effectiveness in node classification tasks. However, traditional GNNs often neglect role-based characteristics that can enhance node representation learning. To overcome these limitations, a new model called the hypergraph neural network with state space model (HGMN) has been proposed, integrating role-aware representations and employing hypergraph construction techniques to capture complex relationships among nodes.

The article presents FlowPath, a novel method for modeling continuous-time dynamics from sparse and irregularly-sampled time series. It addresses the limitations of existing methods that rely on fixed interpolation schemes, which can misrepresent data. FlowPath utilizes an invertible neural flow to create a continuous, data-adaptive manifold, enhancing classification accuracy significantly across 18 benchmark datasets.