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 741–750 of 982 papers

TitleStatusHype
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
HAT-GAE: Self-Supervised Graph Auto-encoders with Hierarchical Adaptive Masking and Trainable Corruption—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
HCGR: Hyperbolic Contrastive Graph Representation Learning for Session-based Recommendation—0
HCL: Improving Graph Representation with Hierarchical Contrastive Learning—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
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Benchmark Results

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