Scan statistics have been a very important and active area of statistical research in the past
three decades. Detecting areas with a significant concentration of points is an important task …

The ability to detect change-points in a dynamic network or a time series of graphs is an
increasingly important task in many applications of the emerging discipline of graph signal …

We consider the problem of detecting a tight community in a sparse random network. This is
formalized as testing for the existence of a dense random subgraph in a random graph …

A network provides powerful means of representing complex relationships between entities
by abstracting entities as vertices, and relationships as edges connecting vertices in a …

We present GraphScan, a novel method for detecting arbitrarily shaped connected clusters
in graph or network data. Given a graph structure, data observed at each node, and a score …

Detecting “hotspots” and “anomalies” is a recurring problem with a wide range of
applications, such as social network analysis, epidemiology, finance, and biosurveillance …

We study the problem of discrepancy maximization on graphs: given a set of nodes Q of an
underlying graph G, we aim to identify a connected subgraph of G that contains many more …

Many empirical networks have community structure, in which nodes are densely
interconnected within each community (ie, a group of nodes) and sparsely across different …

Dense subgraph discovery is a key primitive in many graph mining applications, such as
detecting communities in social networks and mining gene correlation from biological data …

Change point detection is crucial for identifying state transitions and anomalies in dynamic
systems, with applications in network security, health care, and social network analysis …