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 211–220 of 982 papers

TitleStatusHype
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State PredictionCode0
Complete and Efficient Graph Transformers for Crystal Material Property Prediction—0
Graph Partial Label Learning with Potential Cause Discovering—0
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image AnalysisCode2
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
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Benchmark Results

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