Feature Propagation for Learning on Graphs with Missing Node Features [Paper, Blog Post, Presentation, Slides]
While Graph Neural Networks (GNNs) have recently become the de facto standard for modeling relational data, they impose a strong assumption on the availability of the node or edge features of the graph. In many real-world applications, however, features are only partially available; for example, in social networks, age and gender are available only for a small subset of users.
This repo contains the code for Feature Propagation, a general approach for handling missing features in graph machine learning applications that is based on minimization of the Dirichlet energy and leads to a diffusion-type differential equation on the graph. The discretization of this equation produces a simple, fast and scalable algorithm.
We experimentally show that the proposed approach outperforms previous methods on seven common node-classification benchmarks and can withstand surprisingly high rates of missing features: on average we observe only around 4% relative accuracy drop when 99% of the features are missing. Moreover, it takes only 10 seconds to run on a graph with ∼2.5M nodes and ∼123M edges on a single GPU.
Our implementation works with python >= 3.9 and has the following dependencies:
torch==1.10.2
pyg==2.0.3
ogb==1.3.1
which can be installed as follows using conda:
conda create -n feature_propagation python=3.9
conda activate feature_propagation
conda install pytorch=1.10.2 -c pytorch
conda install pyg=2.0.3 -c pyg -c conda-forge
pip install ogb==1.3.2
where cudatoolkit=10.2 would need to be added to the second line if the GPU version of PyTorch is desired (also changing 10.2 to the cuda version of your GPU). Refer to the PyTorch installation page for more details.
Feature Propagation can be run with the following command:
python src/run.py --dataset_name Cora --missing_rate 0.99
where dataset_name can be any of "Cora", "CiteSeer", "PubMed", "OGBN-Arxiv", "OGBN-Products" and missing rate is the rate of missing features.
GCNMF and PaGNN can be run as follows:
python src/run.py --model gcnmf --dataset_name Cora --missing_rate 0.99
python src/run.py --model pagnn --dataset_name Cora --missing_rate 0.99
python src/run.py --model lp --dataset_name Cora --lp_alpha 0.9
The other baselines can be run by specifying the filling_method argument:
python src/run.py --filling_method zero --dataset_name Cora --missing_rate 0.99
python src/run.py --filling_method random --dataset_name Cora --missing_rate 0.99
python src/run.py --filling_method mean --dataset_name Cora --missing_rate 0.99
python src/run.py --filling_method neighborhood_mean --dataset_name Cora --missing_rate 0.99
The results on the synthetic data with variying degree of homophily can be run as follows:
python src/run.py --model sage --dataset_name MixHopSynthetic --missing_rate 0.99 --homophily 0.1
where homophily can be any one of [0.0, 0.1 ..., 0.9].
Main arguments:
--dataset_name Name of dataset
--filling_method Method to solve the missing feature problem
--model Type of model to make a prediction on the downstream task
--missing_rate Rate of node features missing
--num_iterations Number of diffusion iterations for feature reconstruction
@article{rossi2021fp,
title={On the Unreasonable Effectiveness of Feature propagation in Learning on Graphs with Missing Node Features},
author={Emanuele Rossi and Henry Kenlay and Maria I. Gorinova and Ben Chamberlain and Xiaowen Dong and Michael M. Bronstein},
year={2021},
journal={ArXiv},
arXiv={2111.12128},
abbr={arXiv},
}