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 801–825 of 982 papers

TitleStatusHype
PROXI: Challenging the GNNs for Link PredictionCode0
About Graph Degeneracy, Representation Learning and ScalabilityCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
PUMA: Efficient Continual Graph Learning for Node Classification with Graph CondensationCode0
DINE: Dimensional Interpretability of Node EmbeddingsCode0
Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter TuningCode0
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
Graph Pooling via Coarsened Graph InfomaxCode0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
Robust Graph Representation Learning for Local Corruption RecoveryCode0
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
IsoNN: Isomorphic Neural Network for Graph Representation Learning and ClassificationCode0
Is Performance of Scholars Correlated to Their Research Collaboration Patterns?Code0
Topology Only Pre-Training: Towards Generalised Multi-Domain Graph ModelsCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
Querying functional and structural niches on spatial transcriptomics dataCode0
GraphMatcher: A Graph Representation Learning Approach for Ontology MatchingCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease DetectionCode0
Graph Mamba: Towards Learning on Graphs with State Space ModelsCode0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
RDGSL: Dynamic Graph Representation Learning with Structure LearningCode0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
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Benchmark Results

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