User Alignment Across Social Networks Based On ego-Network Embedding

Cross-social network user alignment is to find users with the same identity in multiple social networks. It has important applications in natural and scientific fields, such as link prediction and personality recommendation, and has certain research value in the field of data mining. Most current approaches embed social networks in a low-dimensional vector space and then align users in the low-dimensional space. However, because the social network is extremely complex and large, it is easy to be affected by error propagation and noise of different neighbors in the process of network embedding. Therefore, to obtain better embedding, we first form the user's EGO network, then use the random walk to extract the user node sequence, then use the framework of the natural language model to learn the low-dimensional vector representation of the user, and finally train a matrix to map the two social networks into the same feature space for alignment. Our experiments on real-world data set Foursquare-Twitter and Livejournal-myspace show some improvement over several baseline results.