چكيده فارسي :
Node classification in graph data plays an important
role in web mining applications. We classify the existing node
classifiers into Inductive and Transductive approaches. Among
the Transductive methods, the Majority Rule method (MRM) has
a prominent role. This method considers only the class labels of
the neighboring nodes, neglecting the informative connectivity
information in the graph data. In this paper, we propose an
Augmented Random Walk (ARW) based approach to resolve
main limitations of MRM. In our proposed method, first, we
augment the initial graph by adding class labels as new nodes to
the graph and then we connect each classified node to its
corresponding class label nodes. Second, we apply a Random
Walk algorithm to find the similarity score of each un-classified
node to different class labels. Third, we predict class labels with
the highest scores for the un-classified node. Empirical results
show that our proposed method clearly outperforms the Majority
Rule method in six graph datasets with high homophily
