Titlebook: Algorithms and Models for the Web Graph; 16th International W Konstantin Avrachenkov,Paweł Prałat,Nan Ye Conference proceedings 2019 Spring

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Using Synthetic Networks for Parameter Tuning in Community Detection,ructural properties and communities of various nature. As a result, it is hard (or even impossible) to develop one algorithm suitable for all datasets. A standard machine learning tool is to consider a parametric algorithm and choose its parameters based on the dataset at hand. However, this approac

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Efficiency of Transformations of Proximity Measures for Graph Clustering,formed with a number of functions including the logarithmic function, the power function, and a family of activation functions. Transformations are tested in experiments in which several classical datasets are clustered using the .-Means, Ward, and the spectral method. The analysis of experimental r

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Almost Exact Recovery in Label Spreading,ve high accuracy clustering using efficient computational procedures. Our main goal is to provide a theoretical justification why the graph-based semi-supervised learning works very well. Specifically, for the Stochastic Block Model in the moderately sparse regime, we prove that popular semi-supervi

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Estimating the Parameters of the Waxman Random Graph, thus less numerous. The model has been in continuous use for over three decades with many attempts to match parameters to real networks, but only a few cases where a formal estimator was used. Even then the performance of the estimator was not evaluated. This paper presents both the first evaluatio

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Understanding the Effectiveness of Data Reduction in Public Transportation Networks, a selected station. Although this problem is NP-hard in general, real-world instances are regularly solved almost completely by a set of simple reduction rules. To explain this behavior, we view transportation networks as hitting set instances and identify two characteristic properties, locality an