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 541550 of 982 papers

TitleStatusHype
Causal Machine Learning: A Survey and Open Problems0
Continuous-Time and Multi-Level Graph Representation Learning for Origin-Destination Demand PredictionCode1
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning0
A Representation Learning Framework for Property GraphsCode1
Iso-CapsNet: Isomorphic Capsule Network for Brain Graph Representation LearningCode0
MultiSAGE: a multiplex embedding algorithm for inter-layer link prediction0
An adaptive graph learning method for automated molecular interactions and properties predictionsCode1
Transferable Graph Backdoor Attack0
Comprehensive Analysis of Negative Sampling in Knowledge Graph Representation LearningCode0
Boosting Graph Structure Learning with Dummy NodesCode1
Show:102550
← PrevPage 55 of 99Next →

Benchmark Results

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