There has been a lot of activity in graph representation learning in recent years. Graph
representation learning aims to produce graph representation vectors to represent the …

Graph neural network (GNN) is a formidable deep learning framework that enables the
analysis and modeling of intricate relationships present in data structured as graphs. In …

Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …

Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing
attention from the graph machine learning community in recent years, owing to its capability …

Graph structure learning (GSL), which aims to learn the adjacency matrix for graph neural
networks (GNNs), has shown great potential in boosting the performance of GNNs. Most …

Opioids (eg, oxycodone and morphine) are highly addictive prescription (aka Rx) drugs
which can be easily overprescribed and lead to opioid overdose. Recently, the opioid …

Sports analytics has captured increasing attention since analysis of the various data enables
insights for training strategies, player evaluation, etc. In this paper, we focus on predicting …

Drug-drug interaction (DDI), as a possible result of drug combination treatment, could lead to
adverse physiological reactions and increasing mortality rates of patients. Therefore …

Prescription (aka Rx) drugs can be easily overprescribed and lead to drug abuse or opioid
overdose. Accordingly, a state-run prescription drug monitoring program (PDMP) in the …

Mental health concerns are a critical issue in today's digital age, posing a threat to both
individual and societal well-being and making the identification of at-risk individuals crucial …