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 626–650 of 982 papers

TitleStatusHype
Discriminative Graph Autoencoder—0
Disentangled Generative Graph Representation Learning—0
Disentangling Interpretable Generative Parameters of Random and Real-World Graphs—0
Distribution Preserving Graph Representation Learning—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
div2vec: Diversity-Emphasized Node Embedding—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
Domain Adaptive Graph Classification—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
Dual Graph Representation Learning—0
Dual Space Graph Contrastive Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
Dynamic Graph Representation Learning for Depression Screening with Transformer—0
Dynamic Graph Representation Learning for Passenger Behavior Prediction—0
DyFormer: A Scalable Dynamic Graph Transformer with Provable Benefits on Generalization Ability—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Dynamic Spiking Framework for Graph Neural Networks—0
DySR: A Dynamic Representation Learning and Aligning based Model for Service Bundle Recommendation—0
EBSD Grain Knowledge Graph Representation Learning for Material Structure-Property Prediction—0
EDEN: A Plug-in Equivariant Distance Encoding to Beyond the 1-WL Test—0
Edge but not Least: Cross-View Graph Pooling—0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
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Benchmark Results

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