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 771780 of 982 papers

TitleStatusHype
Catastrophic Forgetting in Deep Graph Networks: an Introductory Benchmark for Graph ClassificationCode1
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models0
Adversarial Graph DisentanglementCode1
Exploiting Edge-Oriented Reasoning for 3D Point-based Scene Graph AnalysisCode1
Size-Invariant Graph Representations for Graph Classification ExtrapolationsCode1
Graph Autoencoder for Graph Compression and Representation LearningCode1
CogDL: A Comprehensive Library for Graph Deep LearningCode2
Towards a Unified Framework for Fair and Stable Graph Representation LearningCode1
Fast Graph Learning with Unique Optimal SolutionsCode1
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training0
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Benchmark Results

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