In online social networks, users with similar interests tend to come together, forming social
communities. Nowadays, user-defined communities become a prominent part of online …

Graphs consisting of vocal nodes (" the vocal minority") and silent nodes (" the silent
majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have …

The data-hungry problem, characterized by insufficiency and low-quality of data, poses
obstacles for deep learning models. Transfer learning has been a feasible way to transfer …

Graph Neural Networks (GNNs) have shown expressive performance on graph
representation learning by aggregating information from neighbors. Recently, some studies …

Graph Convolutional Networks (GCNs) are a popular type of machine learning models that
use multiple layers of convolutional aggregation operations and non-linear activations to …

Given a large graph, can we learn its node embeddings from a smaller summary graph?
What is the relationship between embeddings learned from original graphs and their …

Network embedding has been widely used in social recommendation and network analysis,
such as recommendation systems and anomaly detection with graphs. However, most of …

Measuring the closeness of friendships is an important problem that finds numerous
applications in practice. For example, online gaming platforms often host friendship …

Graph summarization is a useful tool for analyzing large-scale graphs. Some works tried to
preserve original node embeddings encoding rich structural information of nodes on the …

Recently, graph contrastive learning proposes to learn node representations from the
unlabeled graph to alleviate the heavy reliance on node labels in graph neural networks …