PeterGriffinJin/Heterformer
Heterformer: Transformer-based Deep Node Representation Learning on Heterogeneous Text-Rich Networks (KDD 2023)
This project helps researchers and data scientists analyze complex networks where nodes (like people, papers, or products) are connected and also have associated text, such as paper abstracts or product descriptions. It takes raw text data and network structure to produce powerful insights into the relationships and characteristics within the network. Users are typically researchers, data scientists, or analysts working with complex interconnected data.
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Use this if you need to understand or classify individual items (nodes) within a network where both their connections and their descriptive text are important for analysis.
Not ideal if your data lacks either network structure or associated text, or if you are not comfortable with command-line tools and Python scripting for data preparation and model training.
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28
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Jupyter Notebook
License
Apache-2.0
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Last pushed
Feb 16, 2024
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