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 101–150 of 982 papers

TitleStatusHype
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Sparse Decomposition of Graph Neural Networks—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Explanation-Preserving Augmentation for Semi-Supervised Graph Representation LearningCode2
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
Towards Fair Graph Representation Learning in Social Networks—0
Querying functional and structural niches on spatial transcriptomics dataCode0
Information propagation dynamics in Deep Graph Networks—0
A Benchmark on Directed Graph Representation Learning in Hardware Designs—0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
PROXI: Challenging the GNNs for Link PredictionCode0
TopER: Topological Embeddings in Graph Representation Learning—0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
GRE^2-MDCL: Graph Representation Embedding Enhanced via Multidimensional Contrastive Learning—0
Multi-object event graph representation learning for Video Question Answering—0
Ethereum Fraud Detection via Joint Transaction Language Model and Graph Representation Learning—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
Graffin: Stand for Tails in Imbalanced Node Classification—0
Debiasing Graph Representation Learning based on Information Bottleneck—0
When Heterophily Meets Heterogeneous Graphs: Latent Graphs Guided Unsupervised Representation LearningCode1
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration—0
Neural Spacetimes for DAG Representation Learning—0
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
RELIEF: Reinforcement Learning Empowered Graph Feature Prompt TuningCode1
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
Graph Representation Learning via Causal Diffusion for Out-of-Distribution RecommendationCode1
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
Harvesting Textual and Structured Data from the HAL Publication Repository—0
Noise-Resilient Unsupervised Graph Representation Learning via Multi-Hop Feature Quality EstimationCode0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
Scalable Graph Compressed ConvolutionsCode0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
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Benchmark Results

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