# Sparse Diffusion-Convolutional Neural Networks

arXiv: https://arxiv.org/abs/1710.09813 · HTML: https://mlsanity.com/paper/1710.09813/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Oct 2017.

## Built on by (most cited first, 1 of 1)

- [CGGM: A conditional graph generation model with adaptive sparsity for node anomaly detection in IoT networks](https://mlsanity.com/paper/2402.17363/) (2024)

## Abstract (arXiv)

The predictive power and overall computational efficiency of Diffusion-convolutional neural networks make them an attractive choice for node classification tasks. However, a naive dense-tensor-based implementation of DCNNs leads to O(N2) memory complexity which is prohibitive for large graphs. In this paper, we introduce a simple method for thresholding input graphs that provably reduces memory requirements of DCNNs to O(N) (i.e. linear in the number of nodes in the input) without significantly affecting predictive performance.