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 671–680 of 982 papers

TitleStatusHype
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Federated Graph Representation Learning using Self-Supervision—0
Few-Shot Learning on Graphs—0
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
Fine-tuning Vision Language Models with Graph-based Knowledge for Explainable Medical Image Analysis—0
Flurry: a Fast Framework for Reproducible Multi-layered Provenance Graph Representation Learning—0
FMGNN: Fused Manifold Graph Neural Network—0
From random-walks to graph-sprints: a low-latency node embedding framework on continuous-time dynamic graphs—0
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Benchmark Results

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