Signed and directed networks are ubiquitous in real-world applications. However, there has
been relatively little work proposing spectral graph neural networks (GNNs) for such …

Graph neural networks (GNNs) have shown prominent performance on attributed network
embedding. However, existing efforts mainly focus on exploiting network structures, while …

Networks are ubiquitous in many real-world applications (eg, social networks encoding
trust/distrust relationships, correlation networks arising from time series data). While many …

Clustering is a basic technology in data mining, and similarity measurement plays a crucial
role in it. The existing clustering algorithms, especially those for social networks, pay more …

The most develo** issue in analysing complex networks and graph mining is link
prediction, which can be studied for both content and structural-based analysis in a social …

We consider graphs where edges and their signs are added independently at random from
among all pairs of nodes. We establish strong concentration inequalities for adjacency and …

With an increasing number of applications where data can be represented as graphs, graph
neural networks (GNNs) are a useful tool to apply deep learning to graph data. Signed and …

Graph Neural Networks (GNNs) are a powerful tool for analyzing complex systems and
irregular data structures like graphs, revolutionizing tasks like node classification and link …