Jaemin Yoo is PhD student at the Seoul National University.
:: Supervised Belief Propagation in undirected graphs
Given an undirected network where some of the nodes are labeled, how can
we classify the unlabeled nodes with high accuracy? Loopy Belief
Propagation (LBP) is an inference algorithm widely used for this purpose
with various applications including fraud detection, malware detection,
web classification, and recommendation. However, previous methods based
on LBP have problems in modeling complex structures of attributed
networks because they manually and heuristically select the most
important parameter, the propagation strength.
In this talk, I introduce Supervised Belief Propagation (SBP), a
scalable and novel inference algorithm which automatically learns the
optimal propagation strength by a supervised learning. SBP is generally
applicable to attributed networks including weighted and signed networks.
Through extensive experiments, I demonstrate that SBP generalizes
previous LBP-based methods and outperforms previous LBP and RWR based
method in real-world networks.