Scaling Overlapping Clustering

author: Kyle Kloster, NC State University
published: Oct. 12, 2016,   recorded: August 2016,   views: 920

Related Open Educational Resources

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.


Identifying communities plays a central role in understanding the structure of large networks. As practitioners analyze progressively larger networks, it becomes increasingly important to understand the computational complexity of candidate algorithms. We examine the complexity of the link clustering algorithm for overlapping community detection. We provide new, tight bounds for the original implementation and propose modifications to reduce algorithmic complexity. These new bounds are a function of the number of wedges in the graph. Additionally, we demonstrate that for several community detection algorithms, wedges predict runtime better than commonly cited graph features. We conclude by proposing a method to reduce the wedges in a graph by removing high-degree vertices from the network, identifying communities with an optimized version of link clustering, and heuristically matching communities with the removed vertices as a post-processing step. We empirically demonstrate a large reduction in processing time on several common data sets.

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: