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 301–325 of 982 papers

TitleStatusHype
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
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
An Efficient Memory Module for Graph Few-Shot Class-Incremental LearningCode0
HeteroSample: Meta-path Guided Sampling for Heterogeneous Graph Representation Learning—0
Shedding Light on Problems with Hyperbolic Graph Learning—0
Variational Graph Contrastive LearningCode0
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
Non-Euclidean Mixture Model for Social Network EmbeddingCode0
Centrality Graph Shift Operators for Graph Neural NetworksCode0
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
Synergizing LLM Agents and Knowledge Graph for Socioeconomic Prediction in LBSN—0
Sparse Decomposition of Graph Neural Networks—0
Theoretical Insights into Line Graph Transformation on Graph LearningCode0
Bridging Large Language Models and Graph Structure Learning Models for Robust Representation Learning—0
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Benchmark Results

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