ScaleNet: Scale Invariance Learning in Directed Graphs

TL;DR

This paper introduces ScaleNet, a novel graph neural network architecture that incorporates scale invariance through scaled ego-graphs, significantly improving node classification accuracy across diverse graph types.

Contribution

The paper proposes scaled ego-graphs and ScaleNet, a new architecture that leverages multi-scale features for improved graph learning, especially on heterophilic and homophilic graphs.

Findings

Outperforms existing models on seven benchmark datasets.

Achieves state-of-the-art results on five datasets.

Demonstrates effectiveness of scale invariance in graph learning.

Abstract

Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is actually the ego-graph of the center node that is classified. This research extends the scale invariance concept to node classification by drawing an analogy to image processing: just as scale invariance being used in image classification to capture multi-scale features, we propose the concept of ``scaled ego-graphs''. Scaled ego-graphs generalize traditional ego-graphs by replacing undirected single-edges with ``scaled-edges'', which are ordered sequences of multiple directed edges. We empirically assess the performance of the proposed scale invariance in graphs on seven benchmark datasets, across both homophilic and heterophilic structures. Our scale-invariance-based graph learning outperforms inception models derived from random walks by being simpler, faster, and more accurate. The scale invariance explains inception models' success on homophilic graphs and limitations on heterophilic graphs. To ensure applicability of inception model to heterophilic graphs as well, we further present ScaleNet, an architecture that leverages multi-scaled features. ScaleNet achieves state-of-the-art results on five out of seven datasets (four homophilic and one heterophilic) and matches top performance on the remaining two, demonstrating its excellent applicability. This represents a significant advance in graph learning, offering a unified framework that enhances node classification across various graph types. Our code is available at https://github.com/Qin87/ScaleNet/tree/July25.