PROJECT TITLE :

Efficient Embedding of Scale-Free Graphs in the Hyperbolic Plane - 2018

ABSTRACT:

Hyperbolic geometry seems to be intrinsic in many large real networks. We construct and implement a new maximum chance estimation algorithm that embeds scale-free graphs within the hyperbolic area. All previous approaches of comparable embedding algorithms require a minimum of a quadratic runtime. Our algorithm achieves quasi-linear runtime, that makes it the primary algorithm that can embed networks with lots of thousands of nodes in but one hour. We tend to demonstrate the performance of our algorithm on artificial and real networks. In all typical metrics, such as log-likelihood and greedy routing, our algorithm discovers embeddings that are terribly shut to the ground truth.


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