Representation learning for temporal networks using temporal random walk and deep autoencoder

Abstract

Network representation learning is a promising direction towards applying machine learning over graph-structured data. Most of the recent researches focus on embedding static networks to a low dimensional vector space and performing traditional network mining tasks using this latent space. But, to study many complex interactions between real-world entities, we need to model the data into a time-varying network (temporal network), where the edge connectivity patterns may vary with time. We focus on the problem of temporal network representation learning, where the network is represented with edges time-stamped with the time of interaction. We design a random surfing model for the temporal network using a non-homogeneous Markov chain to generate a node similarity matrix. Further, we perform non-linear dimensionality reduction on the node similarity matrix using a denoising deep autoencoder to generate node representations. We also evaluate the quality of the embeddings generated using a temporal link prediction benchmark.