A survey of imbalanced learning on graphs: Problems, techniques, and future directions
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 …
Effective graph analytics, such as graph learning methods, enables users to gain profound …
Nodemixup: Tackling under-reaching for graph neural networks
Graph Neural Networks (GNNs) have become mainstream methods for solving the semi-
supervised node classification problem. However, due to the uneven location distribution of …
supervised node classification problem. However, due to the uneven location distribution of …
A simple but effective approach for unsupervised few-shot graph classification
Graphs, as a fundamental data structure, have proven efficacy in modeling complex
relationships between objects and are therefore found in wide web applications. Graph …
relationships between objects and are therefore found in wide web applications. Graph …
Evidential meta-model for molecular property prediction
KP Ham, L Sael - Bioinformatics, 2023 - academic.oup.com
Motivation The usefulness of supervised molecular property prediction (MPP) is well-
recognized in many applications. However, the insufficiency and the imbalance of labeled …
recognized in many applications. However, the insufficiency and the imbalance of labeled …
Cross-domain few-shot classification via inter-source stylization
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 …
dataset with limited labelled data by exploiting the knowledge of a richly labelled auxiliary …
Learning Hierarchical Task Structures for Few-shot Graph Classification
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 …
samples, where only limited labeled graphs are provided for each class. To solve the …
Generative Few-shot Graph Classification: An Adaptive Perspective
S Wang, J Li - 2023 57th Asilomar Conference on Signals …, 2023 - ieeexplore.ieee.org
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 …
where only a limited number of labeled graphs are provided for each class. To deal with the …