Graph Clustering With Network Structure Indices
published: June 23, 2007, recorded: June 2007, views: 8052
Slides
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.
Description
Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the k -medoids algorithm and the Girvan-Newman method based on edge betweenness centrality. We show that they can be effective at discovering the latent groups or communities that are defined by the link structure of a graph. However, both approaches rely on prohibitively expensive computations, given the size of modern relational data sets. Network structure indices (NSIs) are a proven technique for indexing network structure and efficiently finding short paths. We show how incorporating NSIs into these graph clustering algorithms can overcome these complexity limitations. We also present promising quantitative and qualitative evaluations of the modified algorithms on synthetic and real data sets.
Link this page
Would you like to put a link to this lecture on your homepage?Go ahead! Copy the HTML snippet !
Reviews and comments:
In this video, relationship between pattern analysis, statistical modelling, and computational learning have been explained in a very beautiful manner. This understanding would help students in improving their machine learning skills. Glad to visit https://topicsmill.com/speech/invitat... website for choosing invitational speech topics that made easy for us to prepare papers on them.
Write your own review or comment: