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 76–100 of 982 papers

TitleStatusHype
Mixture of Experts Meets Decoupled Message Passing: Towards General and Adaptive Node ClassificationCode0
Repository-Level Graph Representation Learning for Enhanced Security Patch DetectionCode1
Fine-grained graph representation learning for heterogeneous mobile networks with attentive fusion and contrastive learning—0
A Self-guided Multimodal Approach to Enhancing Graph Representation Learning for Alzheimer's Diseases—0
Expressivity of Representation Learning on Continuous-Time Dynamic Graphs: An Information-Flow Centric Review—0
GQWformer: A Quantum-based Transformer for Graph Representation Learning—0
From ChebNet to ChebGibbsNetCode0
Toward Fair Graph Neural Networks Via Dual-Teacher Knowledge Distillation—0
Perturbation Ontology based Graph Attention Networks—0
GrokFormer: Graph Fourier Kolmogorov-Arnold TransformersCode1
Instance-Aware Graph Prompt Learning—0
TANGNN: a Concise, Scalable and Effective Graph Neural Networks with Top-m Attention Mechanism for Graph Representation LearningCode0
Conditional Distribution Learning on GraphsCode0
A survey on Graph Deep Representation Learning for Facial Expression Recognition—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
Variational Graph Contrastive LearningCode0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Learning From Graph-Structured Data: Addressing Design Issues and Exploring Practical Applications in Graph Representation Learning—0
Post-Hoc Robustness Enhancement in Graph Neural Networks with Conditional Random Fields—0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Query-Efficient Adversarial Attack Against Vertical Federated Graph LearningCode0
Exploring Consistency in Graph Representations:from Graph Kernels to Graph Neural NetworksCode0
DECRL: A Deep Evolutionary Clustering Jointed Temporal Knowledge Graph Representation Learning Approach—0
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Benchmark Results

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