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 391–400 of 982 papers

TitleStatusHype
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product ManifoldCode0
EXGC: Bridging Efficiency and Explainability in Graph CondensationCode0
Hierarchical Topology Isomorphism Expertise Embedded Graph Contrastive LearningCode0
HopfE: Knowledge Graph Representation Learning using Inverse Hopf FibrationsCode0
Event-based Dynamic Graph Representation Learning for Patent Application Trend PredictionCode0
Adversarial Graph Contrastive Learning with Information RegularizationCode0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
Classic Graph Structural Features Outperform Factorization-Based Graph Embedding Methods on Community LabelingCode0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
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Benchmark Results

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