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 851–875 of 982 papers

TitleStatusHype
About Graph Degeneracy, Representation Learning and ScalabilityCode0
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning—0
VeRNAl: Mining RNA Structures for Fuzzy Base Pairing Network MotifsCode0
Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation LearningCode1
OFFER: A Motif Dimensional Framework for Network Representation Learning—0
Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art—0
Tree Structure-Aware Graph Representation Learning via Integrated Hierarchical Aggregation and Relational Metric Learning—0
Efficient Knowledge Graph Validation via Cross-Graph Representation Learning—0
A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices—0
node2coords: Graph Representation Learning with Wasserstein Barycenters—0
Edge-aware Graph Representation Learning and Reasoning for Face ParsingCode1
Graph-based prediction of Protein-protein interactions with attributed signed graph embeddingCode1
Second-Order Pooling for Graph Neural NetworksCode1
Deep Representation Learning For Multimodal Brain Networks—0
Recent Advances in Network-based Methods for Disease Gene PredictionCode0
Towards Deeper Graph Neural NetworksCode1
Are Hyperbolic Representations in Graphs Created Equal?—0
Dynamic Graph Representation Learning for Video Dialog via Multi-Modal Shuffled Transformers—0
Hierarchical and Unsupervised Graph Representation Learning with Loukas's CoarseningCode0
Maximizing Cohesion and Separation in Graph Representation Learning: A Distance-aware Negative Sampling ApproachCode0
GCN-BMP: Investigating Graph Representation Learning for DDI Prediction Task—0
Navigating the Dynamics of Financial Embeddings over Time—0
Structural Landmarking and Interaction Modelling: on Resolution Dilemmas in Graph Classification—0
Hop Sampling: A Simple Regularized Graph Learning for Non-Stationary Environments—0
Time-varying Graph Representation Learning via Higher-Order Skip-Gram with Negative SamplingCode0
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Benchmark Results

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