Prediction of Metrics in Temporal Graphs Using Neural Networks

• Daiane M. Pereira Federal University of Rio de Janeiro
• Rodrigo S. Couto Federal University of Rio de Janeiro

DOI: https://doi.org/10.5753/wperformance.2021.15725

Abstract

Several domains, such as characterization of traffic flow in transportation systems, use basic metrics for temporal graphs, such as the average degree and the clustering coefficient. Despite the applicability of these metrics, the literature does not address techniques for predicting their temporal evolution. To fill this gap, we use neural network models to forecast temporal graph metrics. Thus, we analyze the performance of a multilayer perceptron, a recurrent neural network, and a convolutional neural network. These models are compared with a simple base model, showing satisfactory performance, especially for recurrent and convolutional neural networks.

Keywords: Temporal Graph, Neural Network, RNN, CNN, MLP

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