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 351–375 of 982 papers

TitleStatusHype
Disentangled Generative Graph Representation Learning—0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Dynamic Graph Representation Learning for Passenger Behavior Prediction—0
CEGRL-TKGR: A Causal Enhanced Graph Representation Learning Framework for Temporal Knowledge Graph Reasoning—0
Path-LLM: A Shortest-Path-based LLM Learning for Unified Graph Representation—0
Node Level Graph Autoencoder: Unified Pretraining for Textual Graph Learning—0
Knowledge Probing for Graph Representation Learning—0
Spatial-temporal Graph Convolutional Networks with Diversified Transformation for Dynamic Graph Representation Learning—0
Contrastive Graph Representation Learning with Adversarial Cross-view Reconstruction and Information Bottleneck—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—0
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies—0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
Scalable Graph Compressed ConvolutionsCode0
Your Graph Recommender is Provably a Single-view Graph Contrastive Learning—0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
PolyFormer: Scalable Node-wise Filters via Polynomial Graph TransformerCode0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
The Heterophilic Graph Learning Handbook: Benchmarks, Models, Theoretical Analysis, Applications and Challenges—0
Unsupervised Graph Representation Learning with Inductive Shallow Node EmbeddingCode0
Edge Graph Intelligence: Reciprocally Empowering Edge Networks with Graph Intelligence—0
Differential Encoding for Improved Representation Learning over Graphs—0
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Benchmark Results

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