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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 201–225 of 982 papers

TitleStatusHype
VideoSAGE: Video Summarization with Graph Representation LearningCode2
Graph Neural Networks for Binary Programming—0
HeteroMILE: a Multi-Level Graph Representation Learning Framework for Heterogeneous Graphs—0
Dealing with Missing Modalities in Multimodal Recommendation: a Feature Propagation-based Approach—0
MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs EmbeddingCode0
Variational Graph Auto-Encoder Based Inductive Learning Method for Semi-Supervised Classification—0
Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks—0
ChebMixer: Efficient Graph Representation Learning with MLP Mixer—0
Investigating Similarities Across Decentralized Financial (DeFi) ServicesCode0
GTC: GNN-Transformer Co-contrastive Learning for Self-supervised Heterogeneous Graph RepresentationCode1
Exploring Task Unification in Graph Representation Learning via Generative Approach—0
Spatial-Temporal Graph Representation Learning for Tactical Networks Future State PredictionCode0
Complete and Efficient Graph Transformers for Crystal Material Property PredictionCode0
Graph Partial Label Learning with Potential Cause Discovering—0
Dynamic Graph Representation with Knowledge-aware Attention for Histopathology Whole Slide Image AnalysisCode2
SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning—0
Control-based Graph Embeddings with Data Augmentation for Contrastive Learning—0
Robust Graph Structure Learning under Heterophily—0
HeGAE-AC: heterogeneous graph auto-encoder for attribute completionCode0
Semi-Supervised Graph Representation Learning with Human-centric Explanation for Predicting Fatty Liver Disease—0
Multi-hop Attention-based Graph Pooling: A Personalized PageRank PerspectiveCode0
Decoupling Weighing and Selecting for Integrating Multiple Graph Pre-training TasksCode1
A Survey on Temporal Knowledge Graph: Representation Learning and Applications—0
Negative Sampling in Knowledge Graph Representation Learning: A Review—0
Hierarchical Multi-Relational Graph Representation Learning for Large-Scale Prediction of Drug-Drug InteractionsCode0
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Benchmark Results

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