Many real systems produce network data or highly interconnected data, which can be called
information networks. These information networks form a critical component in modern …

Given a graph with node attributes, what neighborhoods are anomalous? To answer this
question, one needs a quality score that utilizes both structure and attributes. Popular …

Graph data are prevalent in communication networks, social media, and biological networks.
These data, which are often noisy or inexact, can be represented by uncertain graphs …

Given a network with node attributes, how can we identify communities and spot anomalies?
How can we characterize, describe, or summarize the network in a succinct way …

Graphs offer a generic abstraction for modeling entities and the interactions and
relationships between them. Since most realworld graphs evolve over time, there is a need …

With the increasing popularity of social networks, large volumes of graph data are becoming
available. Large graphs are also derived by structure extraction from relational, text, or …

Uncovering subgraphs with an abnormal distribution of attributes reveals much insight into
network behaviors. For example in social or communication networks, diseases or intrusions …

In today's data-driven world, analytical querying, typically based on the data cube concept, is
the cornerstone of answering important business questions and making data-driven …

When the focus is on the relationships or interactions between entities, graphs offer an
intuitive model for many real-world data. Such graphs are usually large and change over …

Emerging applications face the need to store and analyze interconnected data. Graph cubes
permit multi-dimensional analysis of graph datasets based on attribute values available at …