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 481–490 of 982 papers

TitleStatusHype
Feature Propagation on Graph: A New Perspective to Graph Representation Learning—0
Improving Knowledge Graph Representation Learning by Structure Contextual Pre-training—0
Implications of sparsity and high triangle density for graph representation learning—0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
A Survey On Few-shot Knowledge Graph Completion with Structural and Commonsense Knowledge—0
Residual or Gate? Towards Deeper Graph Neural Networks for Inductive Graph Representation Learning—0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
Hierarchical Adaptive Pooling by Capturing High-order Dependency for Graph Representation Learning—0
CommPOOL: An Interpretable Graph Pooling Framework for Hierarchical Graph Representation Learning—0
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Benchmark Results

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