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

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Showing 761–770 of 982 papers

TitleStatusHype
TSGN: Transaction Subgraph Networks for Identifying Ethereum Phishing Accounts—0
QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question AnsweringCode1
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
New Benchmarks for Learning on Non-Homophilous GraphsCode1
Sub-GMN: The Neural Subgraph Matching Network Model—0
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoTCode1
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
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Benchmark Results

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