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 251–275 of 982 papers

TitleStatusHype
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation—0
Tensor Graph Convolutional Network for Dynamic Graph Representation Learning—0
Accurate and Scalable Estimation of Epistemic Uncertainty for Graph Neural Networks—0
Motif-aware Riemannian Graph Neural Network with Generative-Contrastive LearningCode1
DiffKG: Knowledge Graph Diffusion Model for RecommendationCode1
Adversarial Representation with Intra-Modal and Inter-Modal Graph Contrastive Learning for Multimodal Emotion Recognition—0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
PC-Conv: Unifying Homophily and Heterophily with Two-fold FilteringCode1
Domain Adaptive Graph Classification—0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Social Recommendation through Heterogeneous Graph Modeling of the Long-term and Short-term Preference Defined by Dynamic Time SpansCode0
Graph Invariant Learning with Subgraph Co-mixup for Out-Of-Distribution GeneralizationCode1
LightGCN: Evaluated and EnhancedCode0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
Dynamic Spiking Framework for Graph Neural Networks—0
Symmetry Breaking and Equivariant Neural Networks—0
EdgePruner: Poisoned Edge Pruning in Graph Contrastive Learning—0
Isomorphic-Consistent Variational Graph Auto-Encoders for Multi-Level Graph Representation Learning—0
Understanding Community Bias Amplification in Graph Representation Learning—0
Relational Deep Learning: Graph Representation Learning on Relational DatabasesCode1
On the Initialization of Graph Neural NetworksCode0
Large-scale Graph Representation Learning of Dynamic Brain Connectome with Transformers—0
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
Recurrent Distance Filtering for Graph Representation LearningCode1
Normed Spaces for Graph EmbeddingCode0
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Benchmark Results

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