Uncertainty in Artificial Intelligence
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Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model
Myunghwan Kim, Jure Leskovec
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.
Keywords:
Pages: 400-409
PS Link:
PDF Link: /papers/11/p400-kim.pdf
BibTex:
@INPROCEEDINGS{Kim11,
AUTHOR = "Myunghwan Kim and Jure Leskovec",
TITLE = "Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model",
BOOKTITLE = "Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11)",
PUBLISHER = "AUAI Press",
ADDRESS = "Corvallis, Oregon",
YEAR = "2011",
PAGES = "400--409"
}


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