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 7180 of 982 papers

TitleStatusHype
Graph Propagation Transformer for Graph Representation LearningCode1
Towards Better Graph Representation Learning with Parameterized Decomposition & FilteringCode1
Deep Graph Representation Learning and Optimization for Influence MaximizationCode1
Class-Imbalanced Learning on Graphs: A SurveyCode1
Multi-view Tensor Graph Neural Networks Through Reinforced AggregationCode1
Efficient and Feasible Robotic Assembly Sequence Planning via Graph Representation LearningCode1
SUREL+: Moving from Walks to Sets for Scalable Subgraph-based Graph Representation LearningCode1
FTM: A Frame-level Timeline Modeling Method for Temporal Graph Representation LearningCode1
A critical look at the evaluation of GNNs under heterophily: Are we really making progress?Code1
Learnable Topological Features for Phylogenetic Inference via Graph Neural NetworksCode1
Show:102550
← PrevPage 8 of 99Next →

Benchmark Results

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