Social-Dimension Approach to Classification in Large-Scale Networks

Arizona State University, Computer Science and Engineering, Data Mining and Machine Learning


Social media often provides social network information of users. However, the relationship hidden in the connections are inhomogeneous.  Social dimensions are introduced to represent the heterogeneous interactions people interact with each other (latent affiliations one actor is involved in).  Here, we list some of our published work as well as the data sets and code used for experiments.


Below are some of the data sets we have used for experiments in [2] and [3]. All are in matlab format.  Each data set has two variables: network and groups. "network" is a symmetric sparse matrix representing the interaction between users, and "groups" are the groups subscribed by users. We use that as the class labels in our work.





Other Related References


This project is sponsored by AFOSR-FA95500810132.


Updated on 7/29/2010