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 776–800 of 982 papers

TitleStatusHype
Graph Autoencoder for Graph Compression and Representation LearningCode1
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Towards a Unified Framework for Fair and Stable Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Large-Scale Representation Learning on Graphs via BootstrappingCode1
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Calibrating and Improving Graph Contrastive LearningCode0
Graphonomy: Universal Image Parsing via Graph Reasoning and TransferCode1
Generating a Doppelganger Graph: Resembling but DistinctCode1
Boost then Convolve: Gradient Boosting Meets Graph Neural NetworksCode1
SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information MechanismCode1
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Predicting Patient Outcomes with Graph Representation LearningCode1
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Learning Latent Topology for Graph Matching—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
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Benchmark Results

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