Detecting anomalies in data is a vital task, with numerous high-impact applications in areas
such as security, finance, health care, and law enforcement. While numerous techniques …

Networks are ubiquitous in science and have become a focal point for discussion in
everyday life. Formal statistical models for the analysis of network data have emerged as a …

Social media such as blogs, Facebook, Flickr, etc., presents data in a network format rather
than classical IID distribution. To address the interdependency among data instances …

This paper1 is about classifying entities that are interlinked with entities for which the class is
known. After surveying prior work, we present NetKit, a modular toolkit for classification in …

The concept of affordances appeared in psychology during the late 60s as an alternative
perspective on the visual perception of the environment. It was revolutionary in the intuition …

Roles represent node-level connectivity patterns such as star-center, star-edge nodes, near-
cliques or nodes that act as bridges to different regions of the graph. Intuitively, two nodes …

Recent work on graphical models for relational data has demonstrated significant
improvements in classification and inference when models represent the dependencies …

A heterogeneous information network is a network composed of multiple types of objects
and links. Recently, it has been recognized that strongly-typed heterogeneous information …

We analyze a Relational Neighbor (RN) classifier, a simple relational predictive model that
predicts only based on class labels of related neighbors, using no learning and no inherent …

Dependency networks approximate a joint probability distribution over multiple random
variables as a product of conditional distributions. Relational Dependency Networks (RDNs) …