In recent years, neural architecture-based recommender systems have achieved
tremendous success, but they still fall short of expectation when dealing with highly sparse …

Class imbalance (CI) in classification problems arises when the number of observations
belonging to one class is lower than the other. Ensemble learning combines multiple models …

This work develops mixup for graph data. Mixup has shown superiority in improving the
generalization and robustness of neural networks by interpolating features and labels …

The issue of distribution shifts is emerging as a critical concern in graph representation
learning. From the perspective of invariant learning and stable learning, a recently well …

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …

Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …

As training deep learning models on large dataset takes a lot of time and resources, it is
desired to construct a small synthetic dataset with which we can train deep learning models …

Generative self-supervised learning (SSL), especially masked autoencoders, has become
one of the most exciting learning paradigms and has shown great potential in handling …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Graph (structure) augmentation aims to perturb the graph structure through heuristic or
probabilistic rules, enabling the nodes to capture richer contextual information and thus …