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 201–210 of 982 papers

TitleStatusHype
VideoSAGE: Video Summarization with Graph Representation LearningCode2
Graph Neural Networks for Binary Programming—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
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Benchmark Results

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