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 …

Spatial-temporal forecasting has attracted tremendous attention in a wide range of
applications, and traffic flow prediction is a canonical and typical example. The complex and …

This paper studies semi-supervised graph classification, a crucial task with a wide range of
applications in social network analysis and bioinformatics. Recent works typically adopt …

Graph classification is a fundamental problem with diverse applications in bioinformatics
and chemistry. Due to the intricate procedures of manual annotations in graphical domains …

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 …

Although graph neural networks (GNNs) have achieved impressive achievements in graph
classification, they often need abundant task-specific labels, which could be extensively …

Graph Neural Networks (GNNs) have demonstrated great power in many network analytical
tasks. However, graphs (ie, networks) in the real world are usually text-rich, implying that …

Graph classification aims to predict the property of the whole graph, which has attracted
growing attention in the graph learning community. This problem has been extensively …

Graphs have a superior ability to represent relational data, like chemical compounds,
proteins, and social networks. Hence, graph-level learning, which takes a set of graphs as …

Semi-supervised graph domain adaptation, as a subfield of graph transfer learning, seeks to
precisely annotate unlabeled target graph nodes by leveraging transferable features …