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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 451–475 of 982 papers

TitleStatusHype
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Graph Transformers without Positional Encodings—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
GraphVAMPNet, using graph neural networks and variational approach to markov processes for dynamical modeling of biomolecules—0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Few-Shot Learning on Graphs—0
Federated Graph Representation Learning using Self-Supervision—0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
Hyperbolic Graph Representation Learning: A Tutorial—0
Distribution Preserving Graph Representation Learning—0
Implications of sparsity and high triangle density for graph representation learning—0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
div2vec: Diversity-Emphasized Node Embedding—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
Knowledge-enhanced Session-based Recommendation with Temporal Transformer—0
Holder Recommendations using Graph Representation Learning & Link Prediction—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification—0
Hop-Hop Relation-aware Graph Neural Networks—0
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Benchmark Results

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