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 971980 of 982 papers

TitleStatusHype
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
Open Domain Question Answering Using Early Fusion of Knowledge Bases and TextCode0
LinkNBed: Multi-Graph Representation Learning with Entity Linkage0
A Multimodal Translation-Based Approach for Knowledge Graph Representation Learning0
Accurate Text-Enhanced Knowledge Graph Representation Learning0
GESF: A Universal Discriminative Mapping Mechanism for Graph Representation Learning0
Hyperbolic Neural NetworksCode0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning0
Learning to Make Predictions on Graphs with AutoencodersCode0
GraphGAN: Graph Representation Learning with Generative Adversarial NetsCode0
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Benchmark Results

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