SOTAVerified

Graph Representation Learning

The goal of Graph Representation Learning is to construct a set of features (‘embeddings’) representing the structure of the graph and the data thereon. We can distinguish among Node-wise embeddings, representing each node of the graph, Edge-wise embeddings, representing each edge in the graph, and Graph-wise embeddings representing the graph as a whole.

Source: SIGN: Scalable Inception Graph Neural Networks

Papers

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Showing 281–290 of 982 papers

TitleStatusHype
HGCLIP: Exploring Vision-Language Models with Graph Representations for Hierarchical UnderstandingCode1
Careful Selection and Thoughtful Discarding: Graph Explicit Pooling Utilizing Discarded Nodes—0
Cross-View Graph Consistency Learning for Invariant Graph RepresentationsCode0
Classification of developmental and brain disorders via graph convolutional aggregation—0
Temporal Graph Representation Learning with Adaptive Augmentation Contrastive—0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Calibrate and Boost Logical Expressiveness of GNN Over Multi-Relational and Temporal GraphsCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
Graph Representation Learning for Infrared and Visible Image Fusion—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Pi-net-linearError (mm)0.47—Unverified