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 826–850 of 982 papers

TitleStatusHype
Uniting Heterogeneity, Inductiveness, and Efficiency for Graph Representation Learning—0
Sub-GMN: The Neural Subgraph Matching Network Model—0
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization—0
Learning to Represent the Evolution of Dynamic Graphs with Recurrent Models—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Graph Traversal with Tensor Functionals: A Meta-Algorithm for Scalable LearningCode0
Wasserstein Graph Neural Networks for Graphs with Missing Attributes—0
Learning multi-resolution representations of research patterns in bibliographic networksCode0
Calibrating and Improving Graph Contrastive LearningCode0
Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks—0
AGRNet: Adaptive Graph Representation Learning and Reasoning for Face Parsing—0
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Het-node2vec: second order random walk sampling for heterogeneous multigraphs embeddingCode0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
GL-Disen: Global-Local disentanglement for unsupervised learning of graph-level representations—0
Dynamic Graph Representation Learning with Fourier Temporal State EmbeddingCode0
Recursive Neighborhood Pooling for Graph Representation Learning—0
Learning Latent Topology for Graph Matching—0
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning—0
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks—0
Explainable Subgraph Reasoning for Forecasting on Temporal Knowledge Graphs—0
Towards Powerful Graph Neural Networks: Diversity Matters—0
Deep Graph Generators: A Survey—0
Deep Multi-attribute Graph Representation Learning on Protein Structures—0
Hop-Hop Relation-aware Graph Neural Networks—0
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Benchmark Results

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