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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 821–830 of 982 papers

TitleStatusHype
Detection of Fake Users in SMPs Using NLP and Graph Embeddings—0
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
Graph Representation Learning in Biomedicine—0
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Sub-GMN: The Neural Subgraph Matching Network Model—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Show:102550
← PrevPage 83 of 99Next →

Benchmark Results

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