1 research outputs found
Predicting Attributes of Nodes Using Network Structure
In many graphs such as social networks, nodes have associated attributes
representing their behavior. Predicting node attributes in such graphs is an
important problem with applications in many domains like recommendation
systems, privacy preservation, and targeted advertisement. Attributes values
can be predicted by analyzing patterns and correlations among attributes and
employing classification/regression algorithms. However, these approaches do
not utilize readily available network topology information. In this regard,
interconnections between different attributes of nodes can be exploited to
improve the prediction accuracy. In this paper, we propose an approach to
represent a node by a feature map with respect to an attribute (which is
used as input for machine learning algorithms) using all attributes of
neighbors to predict attributes values for . We perform extensive
experimentation on ten real-world datasets and show that the proposed feature
map significantly improves the prediction accuracy as compared to baseline
approaches on these datasets.Comment: This paper is Published at ACM Transactions on Intelligent Systems
and Technology (ACM TIST