FCL T results in the Markov chain setting, an alternative condition that is more applicable for SA problems is established.

Any improvements that are not entangled with a learning objective, i.e., pure pre-processing, usually arising from clues found in the node features and graph structure.

We study pointwise estimation and uncertainty quantification for a sparse varia-tional Gaussian process method with eigenvector inducing variables.

Graph Representational Learning (GRL) have come at the cost of significant computational resource requirements for training, e.g., for calculating gradients

We tackle the problem of novel class discovery and localization (NCDL) .

In dynamical systems, the states of an agent are affected by the interactions, and the states are usually recorded as a set of continuous variables, which make it difficult to uncover the interactions based on the similarity between the agents.