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 …

Identifying emergent leaders in organizations is a key issue in organizational behavioral
research, and a new problem in social computing. This paper presents an analysis on how …

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

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 …

Procedures for collective inference make simultaneous statistical judgments about the same
variables for a set of related data instances. For example, collective inference could be used …

Classification trees are widely used in the machine learning and data mining communities
for modeling propositional data. Recent work has extended this basic paradigm to …

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

XML documents have recently become ubiquitous because of their varied applicability in a
number of applications. Classification is an important problem in the data mining domain …

The most common approach in predictive modeling is to describe cases with feature vectors
(aka design matrix). Many machine learning methods such as linear regression or support …

Many relational domains contain temporal information and dynamics that are important to
model (eg, social networks, protein networks). However, past work in relational learning has …