NES-TL: Network Embedding Similarity-Based Transfer Learning

The transfer learning methodology leverages knowledge from the source domain with abundant training data to the insufficient target domain. Recently, new approaches continue to be developed and used to solve different classification tasks, ranging from public news to videos and to many others. Most transfer learning methods are based on the assumption that both source and target data are in the same feature space or with the same data distribution, which however is not always true in real applications where it would lead to a negative transfer. In order to overcome this hurdle, the multiple-source transfer learning framework is useful. Since many real systems can be represented by networks, how to utilize the structural similarity between different networks so as to increase the transfer effectiveness becomes important. In this paper, the NES specification index is used to quantitatively measure the structural similarity between two networks, based on which a new transfer learning method (named NES-TL) is developed. Experiments on tag popularity prediction in StackExchange Q&A communities verify the effectiveness of the proposed approach, showing that it behaves better than existing baseline methods.