Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model

Myunghwan Kim, Jure Leskovec
Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, PMLR R9:452-466, 2011.

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 this Paper

BibTeX
@InProceedings{pmlr-vR9-kim11a, title = {Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model}, author = {Kim, Myunghwan and Leskovec, Jure}, booktitle = {Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence}, pages = {452--466}, year = {2011}, editor = {Cozman, Fabio and Pfeffer, Avi}, volume = {R9}, series = {Proceedings of Machine Learning Research}, month = {14--17 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r9/main/assets/kim11a/kim11a.pdf}, url = {https://proceedings.mlr.press/r9/kim11a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model %A Myunghwan Kim %A Jure Leskovec %B Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2011 %E Fabio Cozman %E Avi Pfeffer %F pmlr-vR9-kim11a %I PMLR %P 452--466 %U https://proceedings.mlr.press/r9/kim11a.html %V R9 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Kim, M. & Leskovec, J.. (2011). Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model. Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R9:452-466 Available from https://proceedings.mlr.press/r9/kim11a.html. Reissued by PMLR on 04 October 2026.

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