Dynamic Network Embedding by Semantic Evolution

Abstract

Network embedding, which aims to learn the low-dimensional representations of nodes, has attracted increasing attention in various fields such as social networks, paper citation networks and knowledge graphs. At present, most of the network embedding works are based on static networks, that is, the evolution of networks over time is not taken into account. It is more realistic to consider temporal information in network embedding and it could also make the embedding get more abundant information. In this paper, we propose a dynamic network embedding model DynSEM with semantic evolution, to train node embeddings in a sequence of networks over time. The advantage of our method is that it presents an effective inheritance of historical information. Our method uses nonrandom initialization and orthogonal procrustes method to align the node embeddings into common space which makes node embedding able to inheritance information. In particular, in the common space, we train a model to capture the dynamics information of the networks and smooth temporal node embeddings. We evaluate our method comparing it with other methods on three real-world datasets. The experimental results prove the effectiveness of dynamic network embeddings generated by DynSEM model.