@inproceedings{palla2012infinite,
	Abstract = {Latent variable models for network data extract
a summary of the relational structure
underlying an observed network. The simplest
possible models subdivide nodes of the
network into clusters; the probability of a link
between any two nodes then depends only on
their cluster assignment. Currently available
models can be classified by whether clusters
are disjoint or are allowed to overlap. These
models can explain  clustering structure.
Hierarchical Bayesian models provide
a natural approach to capture more complex
dependencies. We propose a model in which
objects are characterised by a latent feature
vector. Each feature is itself partitioned into
disjoint groups (subclusters), corresponding
to a second layer of hierarchy. In experimental
comparisons, the model achieves significantly
improved predictive performance on
social and biological link prediction tasks.
The results indicate that models with a single
layer hierarchy over-simplify real networks.},
	Author = {Palla, Konstantina and Knowles, David A. and Ghahramani, Zoubin},
	Booktitle = {29th International Conference on Machine Learning (ICML 2012)},
	Keywords = {Machine Learning/Statistics},
	Pages = {1607--1614},
	Title = {{An Infinite Latent Attribute Model for Network Data}},
	Url = {http://icml.cc/2012/papers/785.pdf},
	Year = {2012}}

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