Knowledge Propagation over Conditional Independence Graphs
Urszula Chajewska Harsh Shrivastava
Conditional Independence (CI) graph is a special type of a Probabilistic
Graphical Model (PGM) where the feature connections are modeled using an
undirected graph and the edge weights show the partial correlation strength
between the features. Since the CI graphs capture direct dependence between
features, they have been garnering increasing interest within the research
community for gaining insights into the systems from various domains, in
particular discovering the domain topology. In this work, we propose algorithms
for performing knowledge propagation over the CI graphs. Our experiments
demonstrate that our techniques improve upon the state-of-the-art on the
publicly available Cora and PubMed datasets.