Previous work analyzing social networks has mainly focused on binary friendship relations.
However, in online social networks the low cost of link formation can lead to networks with …

Network sampling is integral to the analysis of social, information, and biological networks.
Since many real-world networks are massive in size, continuously evolving, and/or distributed …

Relational autocorrelation is ubiquitous in relational domains. This observed correlation
between class labels of linked instances in a network (eg, two friends are more likely to share …

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 probability …

Relational data offer a unique opportunity for improving the classification accuracy of
statistical models. If two objects are related, inferring something about one object can aid …

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

Diagnosis and correction of performance issues in modern, large-scale distributed systems
can be a daunting task, since a single developer is unlikely to be familiar with the entire …

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 …

From social science to biology, numerous applications often rely on graphlets for intuitive and
meaningful characterization of networks at both the global macro-level as well as the local …

Many scientific fields analyzing and modeling social networks have focused on manually-collected
datasets where the friendship links are sparse (due to the costs of collection) but …