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 31–40 of 982 papers

TitleStatusHype
Learning Long Range Dependencies on Graphs via Random WalksCode1
Graph External Attention Enhanced TransformerCode1
Learning-Based Link Anomaly Detection in Continuous-Time Dynamic GraphsCode1
A Structure-Aware Framework for Learning Device Placements on Computation GraphsCode1
GCondenser: Benchmarking Graph CondensationCode1
PAC-Bayesian Generalization Bounds for Knowledge Graph Representation LearningCode1
Temporal Graph ODEs for Irregularly-Sampled Time SeriesCode1
Unleashing the Potential of Fractional Calculus in Graph Neural Networks with FRONDCode1
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
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Benchmark Results

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