Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …

Community detection is a fundamental problem in social network analysis consisting,
roughly speaking, in unsupervised dividing social actors (modeled as nodes in a social …

With the rapid development of information technologies, various big graphs are prevalent in
many real applications (eg, social media and knowledge bases). An important component of …

Which topics spark the most heated debates on social media? Identifying those topics is not
only interesting from a societal point of view but also allows the filtering and aggregation of …

Performing an accurate clustering analysis is of great significance for us to understand the
behavior of complex networks, and a variety of graph clustering algorithms have, thus, been …

Community detection algorithms are fundamental tools that allow us to uncover
organizational principles in networks. When detecting communities, there are two possible …

Complex networks have been widely adopted to represent a variety of complicated systems.
Given a complex network, it is of great significance to perform accurate clustering for better …

By now, personal life has been invaded by online social networks (OSNs) everywhere. They
intend to move more and more offline lives to online social networks. Therefore, online …

Given a graph G and a vertex q∈ G, the community search query returns a subgraph of G
that contains vertices related to q. Communities, which are prevalent in attributed graphs …

Community detection is a fundamental and widely-studied problem that finds all densely-
connected groups of nodes and well separates them from others in graphs. With the …