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 601–625 of 982 papers

TitleStatusHype
Pre-training Graph Neural Network for Cross Domain Recommendation—0
Prior Information based Decomposition and Reconstruction Learning for Micro-Expression Recognition—0
Privacy-preserving design of graph neural networks with applications to vertical federated learning—0
Probing Negative Sampling Strategies to Learn GraphRepresentations via Unsupervised Contrastive Learning—0
PSLF: A PID Controller-incorporated Second-order Latent Factor Analysis Model for Recommender System—0
Quantifying Challenges in the Application of Graph Representation Learning—0
RandAlign: A Parameter-Free Method for Regularizing Graph Convolutional Networks—0
Reconstruction for Powerful Graph Representations—0
Dynamic Graph Representation Learning via Edge Temporal States Modeling and Structure-reinforced Transformer—0
Recursive Neighborhood Pooling for Graph Representation Learning—0
Reinforced Imitative Graph Representation Learning for Mobile User Profiling: An Adversarial Training Perspective—0
Relating-Up: Advancing Graph Neural Networks through Inter-Graph Relationships—0
Relational Graph Representation Learning for Open-Domain Question Answering—0
Relation-aware Graph Attention Model With Adaptive Self-adversarial Training—0
Relation-weighted Link Prediction for Disease Gene Identification—0
Graph Representation Learning in Biomedicine—0
Representation Learning for Spatial Graphs—0
Representation Learning of Reconstructed Graphs Using Random Walk Graph Convolutional Network—0
Representation Learning on Graphs to Identifying Circular Trading in Goods and Services Tax—0
RESTORE: Graph Embedding Assessment Through Reconstruction—0
Rethinking the Power of Graph Canonization in Graph Representation Learning with Stability—0
Revisiting Embeddings for Graph Neural Networks—0
Revisiting SVD to generate powerful Node Embeddings for Recommendation Systems—0
NodeSig: Binary Node Embeddings via Random Walk Diffusion—0
RobGC: Towards Robust Graph Condensation—0
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Benchmark Results

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