  Web-based interactions can be frequently represented by an attributed graph,
and node clustering in such graphs has received much attention lately. Multiple
efforts have successfully applied Graph Convolutional Networks (GCN), though
with some limits on accuracy as GCNs have been shown to suffer from
over-smoothing issues. Though other methods (particularly those based on
Laplacian Smoothing) have reported better accuracy, a fundamental limitation of
all the work is a lack of scalability. This paper addresses this open problem
by relating the Laplacian smoothing to the Generalized PageRank and applying a
random-walk based algorithm as a scalable graph filter. This forms the basis
for our scalable deep clustering algorithm, RwSL, where through a
self-supervised mini-batch training mechanism, we simultaneously optimize a
deep neural network for sample-cluster assignment distribution and an
autoencoder for a clustering-oriented embedding. Using 6 real-world datasets
and 6 clustering metrics, we show that RwSL achieved improved results over
several recent baselines. Most notably, we show that RwSL, unlike all other
deep clustering frameworks, can continue to scale beyond graphs with more than
one million nodes, i.e., handle web-scale. We also demonstrate how RwSL could
perform node clustering on a graph with 1.8 billion edges using only a single
GPU.
