In this paper, we propose TNE-DNN, an efficient algorithm to construct time-aware embeddings from temporal networks. We also provide a general framework for associating temporal information into a deep learning based network embedding method.

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.

In this work, we propose a time-aware network embedding method which generates node embeddings by capturing the temporal dynamics of evolving networks.

In this research, we provide a detailed study of the latest advancements in the field of network representation learning (also called network embedding).

In this paper, we propose a hybrid similarity measure for link prediction in real world networks. We also propose a scalable method for community structure-based link prediction on large networks.