Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
Abstract: Heterogeneous graphs (HGs) with multiple entity and relation types are common in real-world networks. Heterogeneous graph neural networks (HGNNs) have shown promise for learning HG ...
Abstract: Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art ...
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