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Creators/Authors contains: "Uzuner, Ozlem"

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  1. ; ; ; ( , ISCRAM 2021 Conference Proceedings – 18th International Conference on Information Systems for Crisis Response and Management)
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; ; ; ; ( , Proceedings of the 2021 SIAM International Conference on Data Mining (SDM))
    null (Ed.)
    Graph synthesis is a long-standing research problem. Many deep neural networks that learn about latent characteristics of graphs and generate fake graphs have been proposed. However, in many cases their scalability is too high to be used to synthesize large graphs. Recently, one work proposed an interesting scalable idea to learn and generate random walks that can be merged into a graph. Due to its difficulty, however, the random walk-based graph synthesis failed to show state-of-the-art performance in many cases. We present an improved random walk-based method by using negative random walks. In our experiments with 6 datasets and 8 baseline methods, our method shows the best performance in almost all cases. We achieve both high scalability and generation quality. 
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