Social networks now connect billions of people around the world, where individuals
occupying different positions often represent different social roles and show different …

Network community detection is a hot research topic in network analysis. Although many
methods have been proposed for community detection, most of them only take into …

In this paper, we study the Multi-Round Influence Maximization (MRIM) problem, where
influence propagates in multiple rounds independently from possibly different seed sets, and …

This paper introduces a new graph-theoretical concept of hidden community for analysing
complex networks, which contain both stronger or dominant communities and weak …

Emotion is a status that combines people's feelings, thoughts, and behaviors, and plays a
crucial role in communication among people. Large studies suggest that human emotions …

In this paper, we introduce a new setting for graph embedding, which considers embedding
communities instead of individual nodes. We find that community embedding is not only …

Community detection or graph clustering is crucial to understanding the structure of complex
networks and extracting relevant knowledge from networked data. Latent factor model, eg …

The explosive growth in popularity of social networking leads to the problematic usage. An
increasing number of social network mental disorders (SNMDs), such as Cyber-Relationship …

Mining from neuroimaging data is becoming increasingly popular in the field of healthcare
and bioinformatics, due to its potential to discover clinically meaningful structure patterns …

Community detection in social networks is an important field of research in data mining and
has an abundant literature. Time varying social networks require algorithms that can comply …