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 276–300 of 982 papers

TitleStatusHype
Democratizing Large Language Model-Based Graph Data Augmentation via Latent Knowledge GraphsCode0
Imbalanced Graph Classification with Multi-scale Oversampling Graph Neural NetworksCode0
Hyperparameter-free and Explainable Whole Graph EmbeddingCode0
Autism spectrum disorder classification based on interpersonal neural synchrony: Can classification be improved by dyadic neural biomarkers using unsupervised graph representation learning?Code0
Hyper-SAGNN: a self-attention based graph neural network for hypergraphsCode0
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
Joint Prediction of Audio Event and Annoyance Rating in an Urban Soundscape by Hierarchical Graph Representation LearningCode0
Deep-Steiner: Learning to Solve the Euclidean Steiner Tree ProblemCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Hyperbolic Geometric Graph Representation Learning for Hierarchy-imbalance Node ClassificationCode0
Augment to Interpret: Unsupervised and Inherently Interpretable Graph EmbeddingsCode0
An Empirical Study of Retrieval-enhanced Graph Neural NetworksCode0
Heterogeneous Deep Graph InfomaxCode0
A knowledge graph representation learning approach to predict novel kinase-substrate interactionsCode0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Hyperbolic Neural NetworksCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Decimated Framelet System on Graphs and Fast G-Framelet TransformsCode0
A Hybrid Membership Latent Distance Model for Unsigned and Signed Integer Weighted NetworksCode0
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Benchmark Results

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