An Overview on Network Representation Learning


S Jafar Ali Ibrahim Professor, B Siva Reddy, K Venkata Yaswanth, K Muneswar, S Jaswanth
Dept of CSE, Qis College of Engineering and Technology, Ongole, Prakasam (Dt), India.


Representation learning has proven its usefulness in many activities such as photography and text mining. The goal of network representation learning is to learn distributed vector representation for each vertex in the networks, an essential feature of network analysis is now increasingly recognised. Some techniques of network representation research network systems for learning. In effect, vertices of the network contain rich data (such as text), that cannot be used with the traditional algorithmic frameworks. We suggest DeepWalk in text-associated form, by showing that DeepWalk, a high-tech network representation solution, is equal to matrix factorisation (TADW). In the context of matrix factorisation, TADW introduce text features of vertices in network representation research. Through applying them to the multi classifying of vertices, we compare our system and different baseline methods.The experimental results show that, our method outperforms other baselines on all three datasets, especially when networks are noisy and training ratio is small.