Experiments indicate Random EdgeĬoding can achieve competitive compression performance on real-world networkĭatasets and scales to graphs with millions of nodes and edges. Of the edges and of vertices within an edge. Proven under a class of random graph models that are invariant to permutations Key to our method is bits-backĬoding, which is used to sample edges and vertices without replacement from theĮdge-list in a way that preserves the structure of the graph. Graphs, and requires only integer arithmetic. Urn, the worst-case computational and memory complexities scale quasi-linearlyĪnd linearly with the number of observed edges, making it efficient on sparse Community Hub Lost in Random Play the odds in Lost in Random, a gothic-fairy-tale-inspired action-adventure where every citizen’s fate is determined by a roll of the dice. When paired with a parameter-free model based on Pólya's Download a PDF of the paper titled Random Edge Coding: One-Shot Bits-Back Coding of Large Labeled Graphs, by Daniel Severo and 3 other authors Download PDF Abstract: We present a one-shot method for compressing large labeled graphs called

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