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 111–120 of 982 papers

TitleStatusHype
Haste Makes Waste: A Simple Approach for Scaling Graph Neural Networks—0
ClassContrast: Bridging the Spatial and Contextual Gaps for Node Representations—0
Diss-l-ECT: Dissecting Graph Data with Local Euler Characteristic TransformsCode0
PROXI: Challenging the GNNs for Link PredictionCode0
TopER: Topological Embeddings in Graph Representation Learning—0
Verbalized Graph Representation Learning: A Fully Interpretable Graph Model Based on Large Language Models Throughout the Entire Process—0
Whole-Graph Representation Learning For the Classification of Signed NetworksCode0
Heterogeneous Hyper-Graph Neural Networks for Context-aware Human Activity Recognition—0
NeuroPath: A Neural Pathway Transformer for Joining the Dots of Human ConnectomesCode0
MDL-Pool: Adaptive Multilevel Graph Pooling Based on Minimum Description Length—0
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Benchmark Results

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