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 881–890 of 982 papers

TitleStatusHype
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and InteractionCode0
LGIN: Defining an Approximately Powerful Hyperbolic GNNCode0
Gradient Flow of Energy: A General and Efficient Approach for Entity Alignment DecodingCode0
Gradient-Based Spectral Embeddings of Random Dot Product GraphsCode0
RingFormer: A Ring-Enhanced Graph Transformer for Organic Solar Cell Property PredictionCode0
Gossip and Attend: Context-Sensitive Graph Representation LearningCode0
GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive LearningCode0
RMNA: A Neighbor Aggregation-Based Knowledge Graph Representation Learning Model Using Rule MiningCode0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Massively Parallel Graph Drawing and Representation LearningCode0
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Benchmark Results

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