A Consistent Diffusion-Based Algorithm for Semi-Supervised Classification on Graphs

TL;DR

This paper introduces a modified diffusion-based algorithm for semi-supervised graph classification, demonstrating that centering node temperatures improves consistency and performance on real datasets.

Contribution

The paper identifies a key inconsistency in existing diffusion algorithms and proposes a simple centering modification that enhances accuracy in semi-supervised graph classification.

Findings

Centered temperatures improve algorithm consistency.

Significant performance gains on real data.

Theoretical proof of the importance of centering.

Abstract

Semi-supervised classification on graphs aims at assigning labels to all nodes of a graph based on the labels known for a few nodes, called the seeds. The most popular algorithm relies on the principle of heat diffusion, where the labels of the seeds are spread by thermo-conductance and the temperature of each node is used as a score function for each label. Using a simple block model, we prove that this algorithm is not consistent unless the temperatures of the nodes are centered before classification. We show that this simple modification of the algorithm is enough to get significant performance gains on real data.