A properly extracted subgraph consists of a small number ofcritical neighbors, while excluding irrelevantones.

Graph Structure Learning (GSL), a family of data-centric learning approaches, has garnered substantial attention in recent years. The core concept behind GSL is to jointly optimize the graph structure and the corresponding GNN models.

Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to ashared aligned distribution given only samples from each distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning.

However, the lack of expert-annotated, open-access resources has hindered the analysis of ALS data using advanced deep learning techniques.