Graph embedding
1.9K
Volume
+20%
Growth
regular
About the Topic
Technique used to represent graph-structured data in a continuous vector space, preserving the graph's structural properties. It includes methods like DeepWalk, Node2Vec, GCNs, and GraphSAGE, and is useful for tasks such as node classification, link prediction, and graph clustering. This technique is particularly beneficial for data scientists and researchers working on complex network analysis and machine learning applications involving graph data.
Graph embedding was discovered on June 11th 2020 and it currently has a search volume of 1.9K with a growth of +20%.
Key Indicators
Growth
- Exploding
- Regular
- Peaked
Speed
- Exponential
- Constant
- Stationary
Seasonality
- High
- Medium
- Low
Volatility
- High
- Average
- Low
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