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

Showing 941950 of 982 papers

TitleStatusHype
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)0.47Unverified