@inproceedings{krafft2012topic-partitioned,
author = {Krafft, P. and Moore, J. and Desmarais, B. and Wallach, H.},
title = {Topic-Partitioned Multinetwork Embeddings},
booktitle = {Advances in Neural Information Processing Systems Twenty-Five},
year = {2012},
month = {December},
abstract = {We introduce a new Bayesian
admixture model intended for exploratory analysis of communication
networks—specifically, the discovery and visualization of topic-specific
subnetworks in email data sets. Our model produces principled visualizations of
email networks, i.e., visualizations that have precise mathematical interpretations
in terms of our model and its relationship to the observed data. We validate
our modeling assumptions by demonstrating that our model achieves better link
prediction performance than three state-of-the-art network models and exhibits
topic coherence comparable to that of latent Dirichlet allocation. We showcase
our model’s ability to discover and visualize topic-specific communication patterns
using a new email data set: the New Hanover County email network. We provide an
extensive analysis of these communication patterns, leading us to recommend our
model for any exploratory analysis of email networks or other similarly-structured
communication data. Finally, we advocate for principled visualization as a
primary objective in the development of new network models.},
url = {http://approjects.co.za/?big=en-us/research/publication/topic-partitioned-multinetwork-embeddings/},
edition = {Advances in Neural Information Processing Systems Twenty-Five},
note = {Also presented at the Workshop on Information in Networks, 2012; the Third New Directions in Analyzing Text as Data Conference, 2012; the Fifth Annual Political Networks Conference, 2012},
}