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With the rise of large-scale social networks, network mining has become an important sub-domain of data mining. Generating an efficient network representation is one important challenge in applying machine learning to network data. Recently, representation learning methods are widely used in various domains to generate low dimensional latent features from complex high dimensional data. A significant amount of research effort is made in the past few years to generate node representations from graph-structured data using representation learning methods. Here, we provide a detailed study of the latest advancements in the field of network representation learning (also called network embedding). We first discuss the basic concepts and models of network embedding. Further, we build a taxonomy of network embedding methods based on the type of networks and review the major research works that come under each category. We then cover the major datasets used in network embedding research and describe the major applications of network embedding with respect to various network mining tasks. Finally, we provide various directions for future work which enhance further research.

Graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.