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Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks.
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs).
Graph Neural Networks for Knowledge Enhanced Visual Representation of Paintings
We propose ArtSAGENet, a novel multimodal architecture that integrates Graph Neural Networks (GNNs) and Convolutional Neural Networks (CNNs), to jointly learn visual and semantic-based artistic representations.
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