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 631–640 of 982 papers

TitleStatusHype
div2vec: Diversity-Emphasized Node Embedding—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
Domain Adaptive Graph Classification—0
Drop Edges and Adapt: a Fairness Enforcing Fine-tuning for Graph Neural Networks—0
DTFormer: A Transformer-Based Method for Discrete-Time Dynamic Graph Representation Learning—0
Dual Graph Representation Learning—0
Dual Space Graph Contrastive Learning—0
DyGSSM: Multi-view Dynamic Graph Embeddings with State Space Model Gradient Update—0
Dynamic Community Detection via Adversarial Temporal Graph Representation Learning—0
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Benchmark Results

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