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

Showing 821830 of 982 papers

TitleStatusHype
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease DetectionCode0
Graph Mamba: Towards Learning on Graphs with State Space ModelsCode0
Stochastic Subgraph Neighborhood Pooling for Subgraph ClassificationCode0
RDGSL: Dynamic Graph Representation Learning with Structure LearningCode0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
Deep Network Embedding for Graph Representation Learning in Signed NetworksCode0
Know Your Neighborhood: General and Zero-Shot Capable Binary Function Search Powered by Call GraphletsCode0
Graphine: A Dataset for Graph-aware Terminology Definition GenerationCode0
Strengthening structural baselines for graph classification using Local Topological ProfileCode0
L2G2G: a Scalable Local-to-Global Network Embedding with Graph AutoencodersCode0
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Benchmark Results

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