Ego-splitting Framework: from Non-Overlapping to Overlapping Clusters

author: Alessandro Epasto, Research at Google, Google, Inc.
published: Oct. 9, 2017,   recorded: August 2017,   views: 914

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.
  Delicious Bibliography


We propose a new framework called Ego-splitting for detecting clusters in complex networks which leverage the local structures known as ego-nets (i.e. the subgraph induced by the neighborhood of each node) to de-couple overlapping clusters. Ego-splitting is highly scalable and flexible framework, with provable theoretical guarantees, that reduce the complex overlapping clustering problem to a simpler and more amenable non-overlapping (partitioning) problem. We cann solve community detection in graphs with tents of billions of edges and outperform previous solutions based on ego-nets analysis. More precisely, our framework works in two steps: a local ego- net analysis, and a global graph partitioning. In the local step, we first partition the nodes’ ego-nets using non-overlapping clustering. We then use these clusters to split each node of the graph into its persona nodes that represents the instantiation of the node in its communities. Then, in the global step, we partition these new persona nodes to obtain an overlapping clustering of the original graph.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: