Digital technologies have made networks ubiquitous. A growing body of research is
examining these networks to gain a better understanding of how firms interact with their …

Many individuals on social networking sites provide traits about themselves, such as
interests or demographics. Social networking sites can use this information to provide better …

The popularity of online social networks and social media has increased the amount of
linked data available in Web domains. Relational and Gaussian Markov networks have both …

We present a theoretical analysis framework that shows how ensembles of collective
classifiers can improve predictions for graph data. We show how collective ensemble …

We consider the problem of predicting plausible missing facts in relational data, given a set
of imperfect logical rules. In particular, our aim is to provide bounds on the (expected) …

We present a theoretical analysis framework for relational ensemble models. We show that
ensembles of collective classifiers can improve predictions for graph data by reducing errors …

Research in statistical relational learning has produced a number of methods for learning
relational models from large-scale network data. While these methods have been …

The prevalence of datasets that can be represented as networks has recently fueled a great
deal of work in the area of Relational Machine Learning (RML). Due to the statistical …

Many analysis tasks involve linked nodes, such as people connected by friendship links.
Research on link-based classification (LBC) has studied how to leverage these connections …

Abstract Statistical Relational Learning (SRL) methods have shown that classification
accuracy can be improved by integrating relations between samples. Techniques such as …