Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

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 …

Recommendation systems usually involve exploiting the relations among known features
and content that describe items (content-based filtering) or the overlap of similar users who …

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

Credit card plays a very important rule in today's economy. It becomes an unavoidable part
of household, business and global activities. Although using credit cards provides enormous …

This study examines the use of social network information for customer churn prediction. An
alternative modeling approach using relational learning algorithms is developed to …

Various features come from relational data often used to enhance the prediction of statistical
models. The features increases as the feature space increases. We proposed a framework …

This paper surveys first-order probabilistic languages (FOPLs), which combine the
expressive power of first-order logic with a probabilistic treatment of uncertainty. We provide …

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 …

With the explosive growth of e-commerce and the booming of e-payment, detecting online
transaction fraud in real time has become increasingly important to Fintech business. To …