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RIM: Reliable Influence-based Active Learning on Graphs.

This repository is the official implementation of RIM.

Requirements

To install requirements:

pip install -r requirements.txt

Training

To train the model(s) in the paper:

cd the “example” data

run the python file RIM.py

Results

  1. Accuracy comparison:

  1. GCN performance comparison:

  1. LP performance comparison:

  1. Budget performance comparison:

  1. Efficiency comparison:

  1. Interpretability:

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RIM: Reliable Influence-based Active Learning on Graphs (NeurIPS'21 Spotlight)

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