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 701–725 of 982 papers

TitleStatusHype
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Unsupervised Adversarially-Robust Representation Learning on Graphs—0
Unsupervised Hierarchical Graph Representation Learning with Variational Bayes—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Uplink Scheduling in Federated Learning: an Importance-Aware Approach via Graph Representation Learning—0
Urban Region Profiling via A Multi-Graph Representation Learning Framework—0
Using Graph Representation Learning with Schema Encoders to Measure the Severity of Depressive Symptoms—0
Using Large-scale Heterogeneous Graph Representation Learning for Code Review Recommendations at Microsoft—0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
Virtual Node Tuning for Few-shot Node Classification—0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Wasserstein Hypergraph Neural Network—0
XLVIN: eXecuted Latent Value Iteration Nets—0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
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