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 426–450 of 982 papers

TitleStatusHype
Hyperbolic Neural NetworksCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
ARIEL: Adversarial Graph Contrastive LearningCode0
Cell Attention NetworksCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Embedding Graphs on Grassmann ManifoldCode0
Robust Graph Representation Learning via Neural SparsificationCode0
EGAD: Evolving Graph Representation Learning with Self-Attention and Knowledge Distillation for Live Video Streaming EventsCode0
Benchmarking Graph Representations and Graph Neural Networks for Multivariate Time Series ClassificationCode0
Neural Causal Graph Collaborative FilteringCode0
Conditional Distribution Learning on GraphsCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Graph Representation Learning via Ladder Gamma Variational AutoencodersCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Heterogeneous Deep Graph InfomaxCode0
GT-SVQ: A Linear-Time Graph Transformer for Node Classification Using Spiking Vector QuantizationCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Disentangling, Amplifying, and Debiasing: Learning Disentangled Representations for Fair Graph Neural NetworksCode0
Are Graph Embeddings the Panacea? An Empirical Survey from the Data Fitness PerspectiveCode0
FairDrop: Biased Edge Dropout for Enhancing Fairness in Graph Representation LearningCode0
GTNet: A Tree-Based Deep Graph Learning ArchitectureCode0
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Benchmark Results

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