Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound …

Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-
supervised node classification problem. However, due to the uneven location distribution of …

Graphs, as a fundamental data structure, have proven efficacy in modeling complex
relationships between objects and are therefore found in wide web applications. Graph …

Motivation The usefulness of supervised molecular property prediction (MPP) is well-
recognized in many applications. However, the insufficiency and the imbalance of labeled …

The goal of Cross-Domain Few-Shot Classification (CDFSC) is to accurately classify a target
dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary …

The problem of few-shot graph classification targets at assigning class labels for graph
samples, where only limited labeled graphs are provided for each class. To solve the …

The task of few-shot graph classification aims to assign class labels to graph samples,
where only a limited number of labeled graphs are provided for each class. To deal with the …