  Gradient boosting of regression trees is a competitive procedure for learning
predictive models of continuous data that fits the data with an additive
non-parametric model. The classic version of gradient boosting assumes that the
data is independent and identically distributed. However, relational data with
interdependent, linked instances is now common and the dependencies in such
data can be exploited to improve predictive performance. Collective inference
is one approach to exploit relational correlation patterns and significantly
reduce classification error. However, much of the work on collective learning
and inference has focused on discrete prediction tasks rather than continuous.
%target values has not got that attention in terms of collective inference. In
this work, we investigate how to combine these two paradigms together to
improve regression in relational domains. Specifically, we propose a boosting
algorithm for learning a collective inference model that predicts a continuous
target variable. In the algorithm, we learn a basic relational model,
collectively infer the target values, and then iteratively learn relational
models to predict the residuals. We evaluate our proposed algorithm on a real
network dataset and show that it outperforms alternative boosting methods.
However, our investigation also revealed that the relational features interact
together to produce better predictions.
