Search Results for author:
Found 4 papers, 0 papers with code
Cause and Effect: Can Large Language Models Truly Understand Causality?
The knowledge from ConceptNet enhances the performance of multiple causal reasoning tasks such as causal discovery, causal identification and counterfactual reasoning.
The Intrinsic Scale of Networks is Small
We define the intrinsic scale at which a network begins to reveal its identity as the scale at which subgraphs in the network (created by a random walk) are distinguishable from similar sized subgraphs in a perturbed copy of the network.
Network Lens: Node Classification in Topologically Heterogeneous Networks
We study the problem of identifying different behaviors occurring in different parts of a large heterogenous network.
Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
We propose a novel subgraph image representation for classification of network fragments with the targets being their parent networks.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
