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 351–400 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
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Leveraging Multi-facet Paths for Heterogeneous Graph Representation Learning—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
Heterogeneous Graph Contrastive Learning with Spectral Augmentation—0
Inference of Sequential Patterns for Neural Message Passing in Temporal Graphs—0
Harvesting Efficient On-Demand Order Pooling from Skilled Couriers: Enhancing Graph Representation Learning for Refining Real-time Many-to-One Assignments—0
Graph Representation Learning Strategies for Omics Data: A Case Study on Parkinson's Disease—0
Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs—0
RobGC: Towards Robust Graph Condensation—0
A Scalable and Effective Alternative to Graph Transformers—0
A Unified Graph Selective Prompt Learning for Graph Neural Networks—0
OLGA: One-cLass Graph AutoencoderCode0
Introducing Diminutive Causal Structure into Graph Representation Learning—0
Predicting Genetic Mutation from Whole Slide Images via Biomedical-Linguistic Knowledge Enhanced Multi-label ClassificationCode0
GEFL: Extended Filtration Learning for Graph ClassificationCode0
Enhancing Fairness in Unsupervised Graph Anomaly Detection through DisentanglementCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Augmentation-based Unsupervised Cross-Domain Functional MRI Adaptation for Major Depressive Disorder Identification—0
Port-Hamiltonian Architectural Bias for Long-Range Propagation in Deep Graph Networks—0
Graphlets correct for the topological information missed by random walks—0
HC-GAE: The Hierarchical Cluster-based Graph Auto-Encoder for Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Fair Graph Representation Learning via Sensitive Attribute DisentanglementCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Parameter-Efficient Tuning Large Language Models for Graph Representation Learning—0
Bounding the Expected Robustness of Graph Neural Networks Subject to Node Feature AttacksCode0
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Benchmark Results

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