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 161170 of 982 papers

TitleStatusHype
CCGL: Contrastive Cascade Graph LearningCode1
Edge Representation Learning with HypergraphsCode1
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learningCode1
Evaluating Modules in Graph Contrastive LearningCode1
Self-supervised Graph-level Representation Learning with Local and Global StructureCode1
Graph Barlow Twins: A self-supervised representation learning framework for graphsCode1
Heterogeneous Graph Representation Learning with Relation AwarenessCode1
Multi-Scale Contrastive Siamese Networks for Self-Supervised Graph Representation LearningCode1
Retrieving Complex Tables with Multi-Granular Graph Representation LearningCode1
UniGNN: a Unified Framework for Graph and Hypergraph Neural NetworksCode1
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Benchmark Results

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