Impact of Graph-to-Sequence Conversion Methods on the Accuracy of Graph Generation for Network Simulations

In the field of communication network management, graph-based simulations using network topology models represented as graphs are widely adopted. In graph-based simulations, because there is a limited number of real network graph data that researchers and experts can access, the technique of generating graphs that mimic the features of real networks using graph generative models is essential. In this context, machine learning-based graph generative models have been rapidly advancing recently. In particular, in terms of the accuracy of reproducing the features of generated graphs, sequence data-based graph generative models have been successful. In this paper, we propose a method based on 2
^nd
-order random walk as an alternative to DFS code, which is used for graph-to-sequence conversion in GraphTune, one of the sequence data-based graph generative models. We conducted experiments on a small dataset with limited diversity on a real graph dataset and confirmed that the model using the proposed method is at best 54.68% more accurate than the model using the conventional method.