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 …

Dictionary learning, which approximates data samples by a set of shared atoms, is a
fundamental task in representation learning. However, dictionary learning over graphs …

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 …

StackOverflow, with its vast question repository and limited labeled examples, raise an
annotation challenge for us. We address this gap by proposing RoBERTa+ MAML, a few …

The latent space of pre-trained generative adversarial networks (GANs) is rich in semantic
information, which often becomes highly entangled. It is crucial to identify semantic …

Graph matching is one of the most significant graph analytic tasks in practice, which aims to
find the node correspondence across different graphs. Most existing approaches rely on …

Named Entity Recognition (NER) is an essential steppingstone in the field of natural
language processing. Although promising performance has been achieved by various …