MMRate: Inferring Multi-aspect Diffusion Networks with Multi-pattern Cascades

author: Senzhang Wang, BeiHang University
published: Oct. 7, 2014,   recorded: August 2014,   views: 2221
Categories

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

Description

Inferring diffusion networks from traces of cascades has been extensively studied to better understand information diffusion in many domains. A widely used assumption in previous work is that the diffusion network is homogenous and diffusion processes of cascades follow the same pattern. However, in social media, users may have various interests and the connections among them are usually multi-faceted. In addition, different cascades normally diffuse at different speeds and spread to diverse scales, and hence show various diffusion patterns. It is challenging for traditional models to capture the heterogeneous user interactions and diverse patterns of cascades in social media. In this paper, we investigate a novel problem of inferring multi-aspect diffusion networks with multi-pattern cascades. In particular, we study the effects of various diffusion patterns on the information diffusion process by analyzing users' retweeting behavior on a microblogging dataset. By incorporating aspect-level user interactions and various diffusion patterns, a new model for inferring Multi-aspect transmission Rates between users using Multi-pattern cascades (MMRate) is proposed. We also provide an Expectation Maximization algorithm to effectively estimate the parameters. Experimental results on both synthetic and microblogging datasets demonstrate the superior performance of our approach over the state-of-the-art methods in inferring multi-aspect diffusion networks.

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: