Recently, Graph Neural Networks (GNNs) arouse a lot of research interest and achieve great success in dealing with graph-based data. The basic idea of GNNs is to aggregate neighbor information iteratively. After k iterations, a k-layer GNN can capture ...

Frequent pattern mining is a key area of study that gives insights into the structure and dynamics of evolving networks, such as social or road networks. However, not only does a network evolve, but often the way that it evolves, itself evolves. Thus, ...

Opinion dynamics the research field dealing with how people's opinions form and evolve in a social context? traditionally uses agent-based models to validate the implications of sociological theories. These models encode the causal mechanism that drives ...

Graph data are pervasive in many real-world applications. Recently, increasing attention has been paid on graph neural networks (GNNs), which aim to model the local graph structures and capture the hierarchical patterns by aggregating the information ...

Given a graph G and the desired size k in bits, how can we summarize G within k bits, while minimizing the information loss?

Large-scale graphs have become omnipresent, posing considerable computational challenges. Analyzing such large graphs can be fast ...