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

Showing 811820 of 982 papers

TitleStatusHype
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
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Benchmark Results

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