Scalable modeling of real graphs using Kronecker multiplication

By Jure Leskovec, Christos Faloutsos. 2007

258 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Statistical and Nonlinear Physics

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Citations in the corpus

Generating synthetic data in finance: opportunities, challenges and pitfalls (2020, methodology): “are the Erdös-Rényi model [34], the Barabási-Albert model [7] and Kronecker graphs [53].”
Kronecker Graphs: An Approach to Modeling Networks (2008, methodology): “The current paper builds on our previous work on Kronecker graphs (Leskovec et al., 2005a; Leskovec and Faloutsos, 2007) and is organized as follows: Section 2 briefly surveys the related literature.”
Statistical properties of community structure in large social and information networks (2008, background): “, models in which a hierarchy is given and the linkage probability between pairs of nodes decreases as a function of their distance in the hierarchy [136, 135, 30, 6, 109, 47, 156, 110].”
Microscopic evolution of social networks (2008, methodology): “The maximum-likelihood principle has been typically used to estimate network model parameters [15, 22, 23] or for model selection [4], which often requires expensive computations of high dimensional integrals over all possible node arrival sequences.”
A brief survey on anonymization techniques for privacy preserving publishing of social network data (2008, methodology): “Leskovec and Faloutsos [16] proposed a method to generate a graph fitting the graph properties of a give graph.”
Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters (2008, background): “, models in which a hierarchy is given and the linkage probability between pairs of nodes decreases as a function of their distance in the hierarchy [139, 138, 31, 6, 110, 48, 159, 111].”
When Social Influence Meets Item Inference (2015, methodology): “te good results with a large number of overlapping nodes. Fig. 4(c) and 4(d) demonstrate that QMSampler can effectively preserve community structures7 by presenting the results of community detection [21] on the two synthetic datasets under 30% overlapping ratio. Here, let SynDBLP SynFlickr denote t”
Big Data Analysis with Signal Processing on Graphs: Representation and processing of massive data sets with irregular structure (2014, background): “Kronecker products of scale-free graphs with the same degree distribution are also scale-free and have the sa m distribution [40], [35].”