  Solving classification with graph methods has gained huge popularity in
recent years. This is due to the fact that the data can be intuitively modeled
with graphs to utilize high level features to aid in solving the classification
problem. CULP which is short for Classification Using Link Prediction is a
graph-based classifier. This classifier utilizes the graph representation of
the data and transforms the problem to that of link prediction where we try to
find the link between an unlabeled node and the proper class node for it. CULP
proved to be highly accurate classifier and it has the power to predict the
labels in near constant time. A variant of the classification problem is
multi-label classification which tackles this problem for multi-label data
where an instance can have multiple labels associated to it. In this work, we
extend the CULP algorithm to address this problem. Our proposed extensions
conveys the powers of CULP and its intuitive representation of the data in to
the multi-label domain and in comparison to some of the cutting edge
multi-label classifiers, yield competitive results.
