Graph Evolution via Social Diffusion Processes
published: Oct. 3, 2011, recorded: September 2011, views: 2904
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
We present a new stochastic process, called as Social Diffusion Process (SDP), to address the graph modeling. Based on this model, we derive a graph evolution algorithm and a series of graph-based approaches to solve machine learning problems, including clustering and semi-supervised learning. SDP can be viewed as a special case of Matthew effect, which is a general phenomenon in nature and societies. We use social event as a metaphor of the intrinsic stochastic process for broad range of data. We evaluate our approaches in a large number of frequently used datasets and compare our approaches to other state-of-the-art techniques. Results show that our algorithm outperforms the existing methods in most cases. We also applying our algorithm into the functionality analysis of microRNA and discover biologically interesting cliques. Due to the broad availability of graph-based data, our new model and algorithm potentially have applications in wide range.
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: