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 251–260 of 982 papers

TitleStatusHype
AnchorGT: Efficient and Flexible Attention Architecture for Scalable Graph Transformers—0
Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning—0
Diversified Node Sampling based Hierarchical Transformer Pooling for Graph Representation Learning—0
Dynamic Graph Representation Learning with Neural Networks: A Survey—0
Black-box Gradient Attack on Graph Neural Networks: Deeper Insights in Graph-based Attack and Defense—0
div2vec: Diversity-Emphasized Node Embedding—0
DistTGL: Distributed Memory-Based Temporal Graph Neural Network Training—0
Biphasic Face Photo-Sketch Synthesis via Semantic-Driven Generative Adversarial Network with Graph Representation Learning—0
A bi-diffusion based layer-wise sampling method for deep learning in large graphs—0
Distribution Preserving Graph Representation Learning—0
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Benchmark Results

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