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Understanding the evolutionary patterns of real-world evolving complexsystems such as human interactions, transport networks, biologicalinteractions, and computer networks has important implications in our dailylives. Predicting future links among the nodes in such networks reveals animportant aspect of the evolution of temporal networks. To analyse networks,they are mapped to adjacency matrices, however, a single adjacency matrixcannot represent complex relationships (e.g. temporal pattern), and therefore,some approaches consider a simplified representation of temporal networks butin high-dimensional and generally sparse matrices. As a result, adjacencymatrices cannot be directly used by machine learning models for making networkor node level predictions. To overcome this problem, automated frameworks areproposed for learning low-dimensional vectors for nodes or edges, asstate-of-the-art techniques in predicting temporal patterns in networks such aslink prediction. However, these models fail to consider temporal dimensions ofthe networks. This gap motivated us to propose in this research a new nodeembedding technique which exploits the evolving nature of the networksconsidering a simple three-layer graph neural network at each time step, andextracting node orientation by Given's angle method. To prove our proposedalgorithm's efficiency, we evaluated the efficiency of our proposed algorithmagainst six state-of-the-art benchmark network embedding models, on four realtemporal networks data, and the results show our model outperforms othermethods in predicting future links in temporal networks.

TempNodeEmb:Temporal Node Embedding considering temporal edge influence matrix