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Recently, graph neural networks have become the new state-of-the-art approach to …

In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …

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 …

We analyze graph smoothing with mean aggregation, where each node successively
receives the average of the features of its neighbors. Indeed, it has quickly been observed …

Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism which …

Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

Recent years have witnessed the popularity and success of graph neural networks (GNN) in
various scenarios. To obtain data-specific GNN architectures, researchers turn to neural …

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 …