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 …

There have been rapid developments in model-based clustering of graphs, also known as
block modelling, over the last ten years or so. We review different approaches and …

Our personal social networks are big and cluttered, and currently there is no good way to
organize them. Social networking sites allow users to manually categorize their friends into …

To design a useful recommender system, it is important to understand how products relate to
each other. For example, while a user is browsing mobile phones, it might make sense to …

Social networks such as Facebook, LinkedIn, and Twitter have been a crucial source of
information for a wide spectrum of users. In Twitter, popular information that is deemed …

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

How can a single person understand what's going on in a collection of millions of
documents? This is an increasingly common problem: sifting through an organization's e …

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 …

Topic models such as the latent Dirichlet allocation (LDA) have become a standard staple in
the modeling toolbox of machine learning. They have been applied to a vast variety of data …

People's personal social networks are big and cluttered, and currently there is no good way
to automatically organize them. Social networking sites allow users to manually categorize …