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 626–650 of 982 papers

TitleStatusHype
Robust Graph Representation Learning via Predictive Coding—0
Robust Graph Structure Learning under Heterophily—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
Scalable Hierarchical Embeddings of Complex Networks—0
Scam Detection for Ethereum Smart Contracts: Leveraging Graph Representation Learning for Secure Blockchain—0
scBiGNN: Bilevel Graph Representation Learning for Cell Type Classification from Single-cell RNA Sequencing Data—0
SCGG: A Deep Structure-Conditioned Graph Generative Model—0
Self-Supervised Dynamic Graph Representation Learning via Temporal Subgraph Contrast—0
Self-Supervised Graph Learning with Proximity-based Views and Channel Contrast—0
Self-Supervised Graph Representation Learning via Global Context Prediction—0
Self-supervised Graph Representation Learning via Bootstrapping—0
Self-supervised Graph Representation Learning for Black Market Account Detection—0
Self-supervised Learning and Graph Classification under Heterophily—0
Self-Supervised Graph Representation Learning for Neuronal Morphologies—0
Self-supervision meets kernel graph neural models: From architecture to augmentations—0
Semantic Communication Enhanced by Knowledge Graph Representation Learning—0
Semantic Graph Representation Learning for Handwritten Mathematical Expression Recognition—0
Semantic Random Walk for Graph Representation Learning in Attributed Graphs—0
Semiparametric Nonlinear Bipartite Graph Representation Learning with Provable Guarantees—0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
SGA: A Graph Augmentation Method for Signed Graph Neural Networks—0
SGR: Self-Supervised Spectral Graph Representation Learning—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Siamese Attribute-missing Graph Auto-encoder—0
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Benchmark Results

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