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 421–430 of 982 papers

TitleStatusHype
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Robust Graph Structure Learning under Heterophily—0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
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Benchmark Results

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