M. Kim and J. Leskovec. ICML'12

This is a scientific paper authored by M. Kim and J. Leskovec, and appeared in ICML'12. Below is the paper summary written by Tuan Anh.

Online Version

Latent Multi-group Membership Graph Model.

Summary

This is a paper on block based network analysis and prediction. This work furthers the work by Airoldi. et. al (see Related papers) to the extent that each node/ user can actually belong to more than one block and node features are modeled in addition to link existence. The generative process is given as below (see the figure)

On the figure above, $N$, $L$, $K$ is the number of users, number of user feature categories, and number of blocks respectively. The block membership of node $i$ at bloc $k$ is $\phi _{ik}\in [0,1]$. Note that this model does not require $\sum _{k}\phi _{ik}=1$

Related papers

• Probabilistic graph clustering: Airoldi. et. al. Mixed Membership Stochastic Blockmodels. Journal of Machine Learning Research 9 (2008) 1981-2014
• The paper by Hoff on Multiplicative latent factor models for description and prediction of social networks