![]() Temporal graphs are amongst the best tools to model real-world evolving complex systems such as human interactions, the Internet, biological interactions, transport networks, scientific networks, and other social and technological networks. The results show that our proposed models outperform the state-of-the-art models in most cases based on two evaluation metrics. To improve our model, we have considered time encoding and proposed another extension to our model, TempNodeEmb++. These models are applied to eight dynamic protein–protein interaction networks and three other real-world networks, including dynamic email networks, online college text message networks, and human real contact datasets. Our proposed temporal network-embedding algorithm, TempNodeEmb, is validated by comparing it to seven state-of-the-art benchmark network-embedding models. The proposed algorithm includes a new dynamic node-embedding algorithm that exploits the evolving nature of the networks by considering a simple three-layer graph neural network at each time step and extracting node orientation by using Given’s angle method. This algorithm generates low-dimensional features from large, high-dimensional networks to predict temporal patterns in dynamic networks. Considering that existing models fail to consider the temporal dimensions of the networks, this research proposes a novel temporal network-embedding algorithm for graph representation learning. Learning useful representations of nodes in these networks provides greater predictive power with less computational complexity and facilitates the use of machine learning methods. This research aims to enhance our understanding of the evolution of networks by formulating and solving the link-prediction problem for temporal networks using graph representation learning as an advanced machine learning approach. Predicting future links among the nodes in these dynamic networks has many practical implications. Understanding the evolutionary patterns of real-world complex systems such as human interactions, biological interactions, transport networks, and computer networks is important for our daily lives.
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