The existing graph neural architecture search (GNAS) methods heavily rely on supervised
labels during the search process, failing to handle ubiquitous scenarios where supervisions …

Dynamic heterogeneous graph neural networks (DHGNNs) have been shown to be effective
in handling the ubiquitous dynamic heterogeneous graphs. However, the existing DHGNNs …

Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …

Graph representation learning is an effective tool for facilitating graph analysis with machine
learning methods. Most GNNs, including Graph Convolutional Networks (GCN), Graph …

Graph neural networks (GNNs) have attracted tremendous attention from the graph learning
community in recent years. It has been widely adopted in various real-world applications …

Network embedding can effectively dig out potentially useful information and discover the
relationships and rules which exist in the data, that has attracted increasing attention in …

Information cascade popularity prediction is an important problem in social network content
diffusion analysis. Various facets have been investigated (eg, diffusion structures and …

Existing graph clustering networks heavily rely on a predefined yet fixed graph, which can
lead to failures when the initial graph fails to accurately capture the data topology structure …

Graph embedding aims to extract low-dimensional representation vectors, commonly
referred to as embeddings, from graph data. The generated embeddings simplify …

In order to complete a common goal, team recommendation problems identify an efficient
group of experts who can collectively satisfy a set of required skills. A significant number of …