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 401–410 of 982 papers

TitleStatusHype
A Survey on Malware Detection with Graph Representation Learning—0
Topological Pooling on GraphsCode0
Community detection in complex networks via node similarity, graph representation learning, and hierarchical clustering—0
Spatio-Temporal AU Relational Graph Representation Learning For Facial Action Units DetectionCode0
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
Category-Level Multi-Part Multi-Joint 3D Shape Assembly—0
Structure-Aware Group Discrimination with Adaptive-View Graph Encoder: A Fast Graph Contrastive Learning Framework—0
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
Towards Improved Illicit Node Detection with Positive-Unlabelled LearningCode0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
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Benchmark Results

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