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Inductive Learning of Concept Representations from Library-Scale Corpora with Graph Convolution
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Concept Representation Learning

We consider a graph of papers and concepts. Paper nodes have a title and are connected when they have at least one common author. Concept nodes are featureless. The task is to learn a representation for the featureless concept nodes.

Quick start

Run make to setup a virtual environment, download sample data, and run the experiments. If there are any issues, or the methods should be applied to other data, follow the detailed steps below.


Set up a virtual environment (i.e. virtualenv -p /usr/bin/python3 venv && source venv/bin/activate) and then run:

pip install -r requirements.txt

In case, the required packages are not backwards compatible, run pip install -r requirements-stable.txt instead.


A graph directory graph_dir with three csv files:

  • paper.csv with columns: paper_id, year, title
  • annotation.csv with columns: paper_id, subject
  • authorship.csv with columns: paper_id, author


python3 gcn_cv_sc graph_dir -o model_dir

For more information on hyperparameters, consult python3 -h. Then you can evaluate the resulting embedding model_dir/embedding.csv with the scripts and


Run make experiment to reproduce the experiments from the paper. This will download a sample of 100k research items to construct the graph.


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