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 76–100 of 982 papers

TitleStatusHype
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Learnable Topological Features for Phylogenetic Inference via Graph Neural NetworksCode1
GraphPrompt: Unifying Pre-Training and Downstream Tasks for Graph Neural NetworksCode1
Is Distance Matrix Enough for Geometric Deep Learning?Code1
INCREASE: Inductive Graph Representation Learning for Spatio-Temporal KrigingCode1
LazyGNN: Large-Scale Graph Neural Networks via Lazy PropagationCode1
Simultaneous Linear Multi-view Attributed Graph Representation Learning and ClusteringCode1
Simplifying Subgraph Representation Learning for Scalable Link PredictionCode1
Logical Message Passing Networks with One-hop Inference on Atomic FormulasCode1
A Generalization of ViT/MLP-Mixer to GraphsCode1
Data Augmentation on Graphs: A Technical SurveyCode1
Beyond Smoothing: Unsupervised Graph Representation Learning with Edge Heterophily DiscriminatingCode1
RHCO: A Relation-aware Heterogeneous Graph Neural Network with Contrastive Learning for Large-scale GraphsCode1
GRATIS: Deep Learning Graph Representation with Task-specific Topology and Multi-dimensional Edge FeaturesCode1
Implicit Graphon Neural RepresentationCode1
Unleashing the Power of Graph Data Augmentation on Covariate Distribution ShiftCode1
Geometry-Complete Perceptron Networks for 3D Molecular GraphsCode1
PAGE: Prototype-Based Model-Level Explanations for Graph Neural NetworksCode1
Multi-dimensional Edge-based Audio Event Relational Graph Representation Learning for Acoustic Scene ClassificationCode1
Transformers over Directed Acyclic GraphsCode1
DyTed: Disentangled Representation Learning for Discrete-time Dynamic GraphCode1
Unifying Graph Contrastive Learning with Flexible Contextual ScopesCode1
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Benchmark Results

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