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 926–950 of 982 papers

TitleStatusHype
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Commonsense Knowledge Graph Completion Via Contrastive Pretraining and Node ClusteringCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
FairEdit: Preserving Fairness in Graph Neural Networks through Greedy Graph EditingCode0
A Deep Probabilistic Spatiotemporal Framework for Dynamic Graph Representation Learning with Application to Brain Disorder IdentificationCode0
UniKG: A Benchmark and Universal Embedding for Large-Scale Knowledge GraphsCode0
CGCL: Collaborative Graph Contrastive Learning without Handcrafted Graph Data AugmentationsCode0
Conditional Distribution Learning on GraphsCode0
Exploring the Role of Node Diversity in Directed Graph Representation LearningCode0
Multi-Task Graph AutoencodersCode0
Self-supervised Consensus Representation Learning for Attributed GraphCode0
Multi-View Graph Representation Learning Beyond HomophilyCode0
Multi-View Graph Representation Learning for Answering Hybrid Numerical Reasoning QuestionCode0
An Efficient Loop and Clique Coarsening Algorithm for Graph ClassificationCode0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
DICE: Device-level Integrated Circuits Encoder with Graph Contrastive PretrainingCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
A Deep Probabilistic Framework for Continuous Time Dynamic Graph GenerationCode0
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation LearningCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Union Subgraph Neural NetworksCode0
Accelerating Dynamic Network Embedding with Billions of Parameter Updates to MillisecondsCode0
Self-Supervised Graph Representation Learning via Topology TransformationsCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
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Benchmark Results

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