This survey provides a comprehensive overview of methods for learning representations of Dynamic Heterogeneous Graphs (DHGs). It introduces a unified definition for DHGs, categorizing existing approaches into embedding-based, graph neural network (GNN)-based, and Transformer-based models. The paper also summarizes applications, datasets, and benchmarks, while outlining future research directions in this evolving field. AI
IMPACT Provides a structured overview of methods for learning representations of complex, evolving network data.
RANK_REASON The item is a survey paper on a specific area of machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
- alphaXiv
- arXiv
- CatalyzeX
- Connected Papers
- cs.LG
- DagsHub
- Dynamic Heterogeneous Graphs
- Gotit.pub
- graph neural network
- Graph Representation Learning
- Hugging Face
- Litmaps
- ScienceCast
- scite Smart Citations
- Transformer++
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