Representation learning for dynamic graphs: a survey. (English) Zbl 1498.68243
Summary: Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance. Traditionally, machine learning models for graphs have been mostly designed for static graphs. However, many applications involve evolving graphs. This introduces important challenges for learning and inference since nodes, attributes, and edges change over time. In this survey, we review the recent advances in representation learning for dynamic graphs, including dynamic knowledge graphs. We describe existing models from an encoder-decoder perspective, categorize these encoders and decoders based on the techniques they employ, and analyze the approaches in each category. We also review several prominent applications and widely used datasets and highlight directions for future research.
MSC:
| 68T05 | Learning and adaptive systems in artificial intelligence |
| 68R10 | Graph theory (including graph drawing) in computer science |
| 68T30 | Knowledge representation |
| 68-02 | Research exposition (monographs, survey articles) pertaining to computer science |
Keywords:
graph representation learning; dynamic graphs; knowledge graph embedding; heterogeneous information networksSoftware:
CayleyNets; SplineCNN; DeepDive; YAGO; torchdiffeq; PyG; word2vec; PyTorch-BigGraph; dyngraph2vec; STransE; MovieLens; RelNN; RDF2vec; node2vec; DynGEM; GloVe; Tensor2Tensor; metapath2vec; PyTorch; MXNet; KONECT; ProbLog; DeepWalkReferences:
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