Abstract
Random graphs play a central role in network analysis. The Chung-Lu random graph model is one particularly popular model, which connects nodes according to their desired degrees to form a specific degree distribution in expectation. Despite its popularity, the standard Chung-Lu graph generation algorithms are susceptible to significant degree sequence errors when generating simple graphs. In this manuscript, we suggest multiple methods for improving the accuracy of Chung-Lu graph generation by computing node weights which better recreate the desired output degree sequence. We show that each of our solutions offer a significant improvement in degree sequence accuracy.
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Brissette, C., Liu, D., Slota, G.M. (2023). Correcting Output Degree Sequences in Chung-Lu Random Graph Generation. In: Cherifi, H., Mantegna, R.N., Rocha, L.M., Cherifi, C., Micciche, S. (eds) Complex Networks and Their Applications XI. COMPLEX NETWORKS 2016 2022. Studies in Computational Intelligence, vol 1078. Springer, Cham. https://doi.org/10.1007/978-3-031-21131-7_6
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DOI: https://doi.org/10.1007/978-3-031-21131-7_6
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