Modeling Social Networks with Node Attributes Using the Multiplicative Attribute Graph Model
Abstract
Networks arising from social, technological and natural domains exhibit rich connectivity patterns and nodes in such networks are often labeled with attributes or features. We address the question of modeling the structure of networks where nodes have attribute information. We present a Multiplicative Attribute Graph (MAG) model that considers nodes with categorical attributes and models the probability of an edge as the product of individual attribute link formation affinities. We develop a scalable variational expectation maximization parameter estimation method. Experiments show that MAG model reliably captures network connectivity as well as provides insights into how different attributes shape the network structure.
Cite
Text
Kim and Leskovec. "Modeling Social Networks with Node Attributes Using the Multiplicative Attribute Graph Model." Conference on Uncertainty in Artificial Intelligence, 2011.Markdown
[Kim and Leskovec. "Modeling Social Networks with Node Attributes Using the Multiplicative Attribute Graph Model." Conference on Uncertainty in Artificial Intelligence, 2011.](https://mlanthology.org/uai/2011/kim2011uai-modeling/)BibTeX
@inproceedings{kim2011uai-modeling,
title = {{Modeling Social Networks with Node Attributes Using the Multiplicative Attribute Graph Model}},
author = {Kim, Myunghwan and Leskovec, Jure},
booktitle = {Conference on Uncertainty in Artificial Intelligence},
year = {2011},
pages = {400-409},
url = {https://mlanthology.org/uai/2011/kim2011uai-modeling/}
}