Network embedding plays a significant role in a variety of applications. To capture the
topology of the network, most of the existing network embedding algorithms follow a …

Finding the proper depth $ d $ of a graph convolutional network (GCN) that provides strong
representation ability has drawn significant attention, yet nonetheless largely remains an …

Finding node correspondence across networks, namely multi-network alignment, is an
essential prerequisite for joint learning on multiple networks. Despite great success in …

Network embedding plays an important role in a variety of social network applications.
Existing network embedding methods, explicitly or implicitly, can be categorized into …

A fundamental challenge of bipartite graph representation learning is how to extract
informative node embeddings. Self-Supervised Learning (SSL) is a promising paradigm to …

Graph pre-training strategies have been attracting a surge of attention in the graph mining
community, due to their flexibility in parameterizing graph neural networks (GNNs) without …

Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …

In the big data era, the relationship between entries becomes more and more complex.
Many graph (or network) algorithms have already paid attention to dynamic networks, which …

Graph-based collaborative filtering has emerged as a powerful paradigm for delivering
personalized recommendations. Despite their demonstrated effectiveness, these methods …

Contrastive learning is an effective unsupervised method in graph representation learning.
The key component of contrastive learning lies in the construction of positive and negative …