Graph representation learning aims to effectively encode high-dimensional sparse graph-
structured data into low-dimensional dense vectors, which is a fundamental task that has …

The recently developed unsupervised graph representation learning approaches apply
contrastive learning into graph-structured data and achieve promising performance …

Graph classification is a critical task in numerous multimedia applications, where graphs are
employed to represent diverse types of multimedia data, including images, videos, and …

Graph classification is a crucial task in many real-world multimedia applications, where
graphs can represent various multimedia data types such as images, videos, and social …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Next Point-of-Interest (POI) recommendation is a critical task in location-based services that
aim to provide personalized suggestions for the user's next destination. Previous works on …

Semi-supervised node classification is a crucial challenge in relational data mining and has
attracted increasing interest in research on graph neural networks (GNNs). However …

This paper investigates traffic forecasting, which attempts to forecast the future state of traffic
based on historical situations. This problem has received ever-increasing attention in …

Graph classification, aiming at learning the graph-level representations for effective class
assignments, has received outstanding achievements, which heavily relies on high-quality …

Graph classification has gained growing attention in the graph machine learning community
and a variety of semi-supervised methods have been developed to reduce the high cost of …