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 876–900 of 982 papers

TitleStatusHype
Local2Global: Scaling global representation learning on graphs via local trainingCode0
Local Distance-Preserving Node Embeddings and Their Performance on Random GraphsCode0
WikiGraphs: A Wikipedia Text - Knowledge Graph Paired DatasetCode0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Companion Animal Disease Diagnostics based on Literal-aware Medical Knowledge Graph Representation LearningCode0
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule MiningCode0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Massively Parallel Graph Drawing and Representation LearningCode0
Material Prediction for Design Automation Using Graph Representation LearningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
Maximizing Mutual Information Across Feature and Topology Views for Learning Graph RepresentationsCode0
MDGCF: Multi-Dependency Graph Collaborative Filtering with Neighborhood- and Homogeneous-level DependenciesCode0
Memory-based Message Passing: Decoupling the Message for Propogation from DiscriminationCode0
AugWard: Augmentation-Aware Representation Learning for Accurate Graph ClassificationCode0
GL-Coarsener: A Graph representation learning framework to construct coarse grid hierarchy for AMG solversCode0
MGTCOM: Community Detection in Multimodal GraphsCode0
Get Rid of Suspended Animation Problem: Deep Diffusive Neural Network on Graph Semi-Supervised ClassificationCode0
CommunityGAN: Community Detection with Generative Adversarial NetsCode0
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Benchmark Results

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