Event Entry
New for: D2, D3
What and Who
Exploiting Graph-Structured Data in Generative Probebilistic Models
Date, Time and Location
Abstract
data is absent, and thus, supervised machine learning cannot be applied.
These algorithms build on assumptions about how data and predictions
relate to each other. One technique for unsupervised problem settings
are generative models, which specify the set of assumptions as a
probabilistic process that generates the data.
The subject of this thesis is how to most effectively exploit input data
that has an underlying graph structure in unsupervised learning for
three important use cases. The first use case deals with localizing
defective code regions in software, given the execution graph of code
lines and transitions. Citation networks are exploited in the next use
case to quantify the influence of citations on the content of the citing
publication. In the final use case, shared tastes of friends in a social
network are identified, enabling the prediction of items from a user a
particular friend of his would be interested in.
For each use case, prediction performance is evaluated via held-out test
data that is only scarcely available in the domain. This comparison
quantifies under which circumstances each generative model best exploits
the given graph structure.
Contact