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 826–850 of 982 papers

TitleStatusHype
Local Structure-aware Graph Contrastive Representation Learning—0
Machine Learning Partners in Criminal Networks—0
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space—0
Marginalized graph autoencoder for graph clustering—0
Message passing all the way up—0
Mini-Game Lifetime Value Prediction in WeChat—0
Mitigating Relational Bias on Knowledge Graphs—0
Mixed-Curvature Transformers for Graph Representation Learning papersreview—0
Model-Agnostic and Diverse Explanations for Streaming Rumour Graphs—0
Modeling Event Propagation via Graph Biased Temporal Point Process—0
MTLSO: A Multi-Task Learning Approach for Logic Synthesis Optimization—0
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal—0
Multi-Channel Graph Convolutional Networks—0
Multi-fidelity Stability for Graph Representation Learning—0
Multi-Granular Attention based Heterogeneous Hypergraph Neural Network—0
Multi-Level Graph Contrastive Learning—0
Multi-modal Graph Learning for Disease Prediction—0
Multimodal Graph Representation Learning for Robust Surgical Workflow Recognition with Adversarial Feature Disentanglement—0
Multimodal Spatio-temporal Graph Learning for Alignment-free RGBT Video Object Detection—0
Multi-object event graph representation learning for Video Question Answering—0
MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction—0
Multivariate Time Series Classification with Hierarchical Variational Graph Pooling—0
Multi-View MOOC Quality Evaluation via Information-Aware Graph Representation Learning—0
Multi-View Node Pruning for Accurate Graph Representation—0
MxPool: Multiplex Pooling for Hierarchical Graph Representation Learning—0
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Benchmark Results

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