Abstract: Understanding the underlying graph structure of a nonlinear map over a particular domain is essential in evaluating its potential for real applications. In this paper, we investigate the ...
Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
Abstract: Equivariant quantum graph neural networks (EQGNNs) offer a potentially powerful method to process graph data. However, existing EQGNN models only consider the permutation symmetry of graphs, ...
Data analysis is no longer a specialist skill reserved for analysts. It now supports finance, trading, ecommerce, marketing, ...
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