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 911–920 of 982 papers

TitleStatusHype
Molecular Graph Representation Learning Integrating Large Language Models with Domain-specific Small ModelsCode0
Molecular Graph Representation Learning via Structural Similarity InformationCode0
From ChebNet to ChebGibbsNetCode0
Frameless Graph Knowledge DistillationCode0
Features Based Adaptive Augmentation for Graph Contrastive LearningCode0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
MSNGO: multi-species protein function annotation based on 3D protein structure and network propagationCode0
Temporal Graph Offset Reconstruction: Towards Temporally Robust Graph Representation LearningCode0
Community-Aware Temporal Walks: Parameter-Free Representation Learning on Continuous-Time Dynamic GraphsCode0
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network MotifsCode0
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Benchmark Results

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