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

We provide the first information theoretical tight analysis for inference of latent community
structure given a sparse graph along with high dimensional node covariates, correlated with …

Graph clustering, also known as community detection, is a long-standing problem in data
mining. However, with the proliferation of rich attribute information available for objects in …

Many databases contain uncertain and imprecise references to real-world entities. The
absence of identifiers for the underlying entities often results in a database which contains …

Clustering a graph, ie, assigning its nodes to groups, is an important operation whose best
known application is the discovery of communities in social networks. Graph clustering and …

Covert networks are often difficult to reason about, manage and destabilize. In part, this is
because they are a complex adaptive system. In addition, this is due to the nature of the data …

Biological and social systems consist of myriad interacting units. The interactions can be
represented in the form of a graph or network. Measurements of these graphs can reveal the …

Entity resolution has received considerable attention in recent years. Given many references
to underlying entities, the goal is to predict which references correspond to the same entity …

Attributed graphs with both topological information and node information have prevalent
applications in the real world, including recommendation systems, biological networks …

Recent developments in attributed network clustering combine graph neural networks and
autoencoders for unsupervised learning. Although effective, these techniques suffer from …