Knowledge Propagation over Conditional Independence Graphs

• URL: http://arxiv.org/abs/2308.05857v1
• Date: Thu, 10 Aug 2023 21:06:18 GMT
• Title: Knowledge Propagation over Conditional Independence Graphs
• Authors: Urszula Chajewska, Harsh Shrivastava
• Abstract summary: Conditional Independence (CI) graphs capture dependence between features. 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.
• Score: 2.692919446383274
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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