Systems and methods to detect clusters in graphs
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United States of America Patent
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Nov 28, 2023
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N/A
app pub date -
Feb 14, 2018
filing date -
May 2, 2017
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Abstract
The present disclosure provides a new framework and associated techniques, referred to herein as “ego-splitting,” that enable the detection of clusters in graphs that are descriptive of networks, including highly complex networks. Ego-splitting leverages local structures within a graph known as ego-nets to de-couple overlapping clusters. For example, an ego-net can be the subgraph induced by the neighborhood of each node. Ego-splitting is a highly scalable and flexible framework, with provable theoretical guarantees. Ego-splitting reduces the complex overlapping clustering problem to a simpler and more amenable non-overlapping (also known as partitioning) problem. Ego-splitting enables the scaling of community detection to graphs with tens of billions of edges and outperforms previous solutions.
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