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 731–740 of 982 papers

TitleStatusHype
HGPROMPT: Bridging Homogeneous and Heterogeneous Graphs for Few-shot Prompt Learning—0
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning—0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
Open Domain Question Answering Using Early Fusion of Knowledge Bases and TextCode0
Universal Graph Transformer Self-Attention NetworksCode0
Cell Attention NetworksCode0
DyTSCL: Dynamic graph representation via tempo-structural contrastive learningCode0
dyngraph2vec: Capturing Network Dynamics using Dynamic Graph Representation LearningCode0
DynGL-SDP: Dynamic Graph Learning for Semantic Dependency ParsingCode0
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Benchmark Results

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