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 621–630 of 982 papers

TitleStatusHype
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Revisiting Embeddings for Graph Neural Networks—0
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems—0
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
RobGC: Towards Robust Graph Condensation—0
Robust Graph Representation Learning via Predictive Coding—0
Robust Graph Structure Learning under Heterophily—0
Toward Enhanced Robustness in Unsupervised Graph Representation Learning: A Graph Information Bottleneck Perspective—0
SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection—0
Scalable Hierarchical Embeddings of Complex Networks—0
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Benchmark Results

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