  In this work, Transition Probability Matrix (TPM) is proposed as a new method
for extracting the features of nodes in the graph. The proposed method uses
random walks to capture the connectivity structure of a node's close
neighborhood. The information obtained from random walks is converted to
anonymous walks to extract the topological features of nodes. In the embedding
process of nodes, anonymous walks are used since they capture the topological
similarities of connectivities better than random walks. Therefore the obtained
embedding vectors have richer information about the underlying connectivity
structure. The method is applied to node classification and link prediction
tasks. The performance of the proposed algorithm is superior to the
state-of-the-art algorithms in the recent literature. Moreover, the extracted
information about the connectivity structure of similar networks is used to
link prediction and node classification tasks for a completely new graph.
