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 431–440 of 982 papers

TitleStatusHype
LocalGCL: Local-aware Contrastive Learning for Graphs—0
Representation learning in multiplex graphs: Where and how to fuse information?Code0
Hyperdimensional Representation Learning for Node Classification and Link Prediction—0
Understanding Survey Paper Taxonomy about Large Language Models via Graph Representation Learning—0
Adversarial Curriculum Graph Contrastive Learning with Pair-wise Augmentation—0
Position: Topological Deep Learning is the New Frontier for Relational Learning—0
Graph Mamba: Towards Learning on Graphs with State Space ModelsCode0
ExGRG: Explicitly-Generated Relation Graph for Self-Supervised Representation Learning—0
Two Trades is not Baffled: Condensing Graph via Crafting Rational Gradient MatchingCode0
On provable privacy vulnerabilities of graph representations—0
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Benchmark Results

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