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 …

This paper is the first to use contrastive learning to improve the robustness of graph
representation learning for signed bipartite graphs, which are commonly found in social …

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 …

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 …

There have been tremendous efforts over the past decades dedicated to the generation of
realistic graphs in a variety of domains, ranging from social networks to computer networks …

In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Graph neural networks (GNNs) have exhibited superb power in many graph related tasks.
Existing GNNs can be categorized into spatial GNNs and spectral GNNs. The spatial GNNs …

Mixup, which generates synthetic training samples on the data manifold, has been shown to
be highly effective in augmenting Euclidean data. However, finding a proper data manifold …