Search Results for author:
Found 3 papers, 1 papers with code
A neuro-symbolic framework for answering conjunctive queries
The problem of answering logical queries over incomplete knowledge graphs is receiving significant attention in the machine learning community.
Graph Neural Networks with Local Graph Parameters
We propose local graph parameter enabled GNNs as a framework for studying the latter kind of approaches and precisely characterize their distinguishing power, in terms of a variant of the WL test, and in terms of the graph structural properties that they can take into account.
The Logical Expressiveness of Graph Neural Networks
We show that this class of GNNs is too weak to capture all FOC2 classifiers, and provide a syntactic characterization of the largest subclass of FOC2 classifiers that can be captured by AC-GNNs.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
